reliability analysis of intelligent manufacturing systems
TRANSCRIPT
Reliability Analysis of Intelligent ManufacturingSystems Based on Improved FMEA Combined WithMachine LearningChunyan Duan
Tongji University School of Mechanical EngineeringMengshan Zhu ( [email protected] )
Tongji University School of Economics and Management https://orcid.org/0000-0001-7576-0854Kangfan Wang
Tongji University School of Mechanical EngineeringWenyong Zhou
Tongji University School of Economics and Management
Original Article
Keywords: failure mode and effects analysis, reliability analysis, intelligent manufacturing systems,machine learning
Posted Date: October 11th, 2021
DOI: https://doi.org/10.21203/rs.3.rs-957551/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
Title page
Reliability Analysis of Intelligent Manufacturing Systems Based on Improved FMEA Combined with Machine Learning
Chunyan Duan, born in 1987, is currently an assistant professor at School of Mechanical Engineering, Tongji University, China. Her main research interests include intelligent manufacturing and reliability analysis. Tel: +8618817878502; E-mail: [email protected]
Mengshan Zhu, born in 1997, is currently a PhD candidate at School of Economics and Management, Tongji University, China. Her main research interests include intelligent manufacturing and reliability analysis.
Tel: +8615273825050; E-mail: [email protected]
Kangfan Wang, born in 1998, is currently a staff at China Railway Construction Engineering Group. He received his bachelor degree from Tongji University, China in 2021. His main research interests include intelligent manufacturing and reliability analysis.
Wenyong Zhou, born in 1969, is currently a professor and a PhD candidate supervisor at School of Economics and Management, Tongji University, China. His main research interests include quality management and reliability analysis. E-mail: [email protected]
Corresponding author: Mengshan Zhu E-mail: [email protected]
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ORIGINAL ARTICLE
Reliability Analysis of Intelligent Manufacturing Systems Based on Improved FMEA Combined with Machine Learning
Chunyan Duan1, Mengshan Zhu2*, Kangfan Wang1, Wenyong Zhou21
Abstract: Along with the booming of intelligent manufacturing, the reliability management of
intelligent manufacturing systems appears increasingly important. Failure mode and effects analysis
(FMEA) is a prospective reliability management instrument extensively utilized to manage failure
modes of systems, products, processes, and services in various industries. However, the
conventional FMEA method has been criticized for its inherent limitations. Therefore, this paper
devises a method based on improved FMEA model combined with machine learning for complex
systems and applies it to the reliability management of intelligent manufacturing systems. The
structured network of failure modes is constructed based on the knowledge graph for the intelligent
manufacturing systems. The grey relation analysis (GRA) is applied to determine the risk
prioritization of failure modes, hereafter the clustering analysis is employed to extract the features
of failure modes. The results show that the proposed method can more accurately reflect the
coupling relationship between the failure modes compared with the conventional FMEA method.
This research provides significant support for the reliability and risk management of complex
systems such as intelligent manufacturing systems.
Key Words: failure mode and effects analysis, reliability analysis, intelligent manufacturing
systems, machine learning
1 Introduction
The upsurge of the global intelligent manufacturing revolution has promoted the continuous
deepening of the integration of advanced manufacturing technology and new-generation
information technology [1]. Countries around the world are actively participating in this revolution
and have formulated relevant strategic plans, such as National Strategic Plan for Advanced
Manufacturing in the US, Industry 4.0 in the Germany, New Industrial France, The future of
manufacturing: a new era of opportunity and challenge for the UK and so on. Based on the
reshaping of the international industrial pattern and the challenges confronted by the Chinese
traditional manufacturing industry, China put forward the made in China 2025 plan in 2015. In
recent years, China has continuously issued relevant policies, laws, and regulations to vigorously
Mengshan Zhu
1 School of Mechanical Engineering, Tongji University, Shanghai 201804, China
2 School of Economics and Management, Tongji University, Shanghai 200092, China
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support manufacturing enterprises and promote their transformation and upgrading, to accelerate
the transition from a massive manufacturing country to a manufacturing powerhouse. Significantly,
the intelligent manufacturing systems are crucial to realize the digitalization, networking, and
automation of the manufacturing industry. Due to the complexity, it is difficult to manage the risks
of the intelligent manufacturing systems. Therefore, the reliability analysis of intelligent
manufacturing systems is of great significance to the high-quality development of intelligent
manufacturing, and many scholars have done related researches. For instance, He et al. [2] proposed
an integrated predictive maintenance (PdM) strategy to improve the mission reliability of
manufacturing systems and the quality of products. Wan et al. [3] developed an extended FMEA
applied to evaluate risks in the intelligent manufacturing process, which was based on rough sets
and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), and introduced
environmental impacts as one of the risk factors. Chen et al. [4] established a reliability evaluation
model for multistate intelligent manufacturing systems based on operational quality data.
Identification and evaluation of the risks of failures are essential to improve the maintenance
strategy and management. It obtains more importance in complex engineering systems [5]. To
achieve this, several techniques such as failure mode and effects analysis (FMEA), fault tree analysis
(FTA), reliability block diagram (RBD), Monte-Carlo simulation (MCS), Markov analysis (MA),
and Bayesian networks (BN) have been developed and applied [6]. Among them, FMEA is a
structured and proactive reliability management technology utilized to enhance the safety and
reliability of systems, products, processes, and services [7].
So far, FMEA is still one of the most valuable and effective reliability analysis methods used in
various industries [7, ]. Arabian-Hoseynabadi et al. [9] verified that the FMEA method had the
potential to improve the reliability of the wind turbine (WT) system, especially for the offshore
environment. Li and Zhou [10] used FMEA to construct the reliability analysis method of urban gas
transmission and distribution system. Tazi et al. [11] developed a hybrid cost-FMEA analysis for
reliability analysis of wind turbine systems. Wang et al. [12] presented a new FMEA model
combined with the house of reliability (HoR) and rough VIsekriterijumska optimizacija i
KOmpromisno Resenje (VIKOR) approach, and demonstrated the effectiveness of the model for
the transmission system of a vertical machining center.
In the conventional FMEA method, the risk ranking of each failure mode is determined by risk
priority number (RPN) which is calculated by multiplying the values of the risk factors severity (S),
occurrence (O), and detection (D) [13]. However, the conventional FMEA method has inherent
limitations such as ignoring the weight of risk factors, getting the same RPN that may have different
meanings of the failure mode risk, and lacking scientific bases for the calculation of the RPN
[8,1416]. In order to overcome the deficiencies associated with the conventional FMEA method,
various methods have been used to improve the FMEA model. On the one hand, the weight of risk
factors is considered in the risk analysis. Subjective weight methods such as analytic hierarchy
process (AHP) [7], objective weight methods such as entropy weight method [17], or comprehensive
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weight methods [18-20] to give different weights to risk factors. On the other hand, many
approaches have been applied to increase the reliability and rationality of risk prioritization. Bian et
al. [21] proposed a new risk prioritization model based on D numbers and TOPSIS to evaluate the
risk in FMEA. Kumar et al. [22] applied fuzzy FMEA and fuzzy logic with the grey relational
approach (GRA) to rank the identified failure modes. Baghery et al. [23] prioritized the
manufacturing processes based on the process failure mode and effects analysis (PFMEA), interval
data envelopment analysis and grey relational analysis. Tian et al. [24] established an integrated
fuzzy MCDM approach for FMEA, and a fuzzy VIKOR approach was employed to obtain the risk
priorities of failure modes. Huang et al. [8] integrated probabilistic linguistic term sets and TODIM
(an acronym in Portuguese for interactive multi-criteria decision making) method to evaluate and
prioritize the risk of failure modes. Because of the powerful data processing capabilities of machine
learning, it has also become one of the improvement directions of the conventional FMEA method.
For example, Ku et al. [25] proposed a BPN-based FMEA system (N-FMEA) for the failure modes
classification and the reliability calculation. Keskin and Ozkan [26] introduced Fuzzy Adaptive
Resonance Theory (Fuzzy ART), which was developed for clustering problems in artificial neural
networks. Jomthanachai et al. [27] integrated DEA and machine learning for risk assessment.
When conducting reliability analysis of large-scale systems, due to a large number of failure
modes and complex relationships, it is possible to consider building a structured network of failure
modes. The knowledge graph is a structured semantic network used to represent the relationship
between entities, and has powerful capability for visualization and knowledge reasoning. It provides
semantically structured information that is interpretable by computers, which is regarded as an
important ingredient to build more intelligent machines [28]. Applying the knowledge graph to
failure mode and effects analysis is greatly promotable for the intelligence of reliability management.
Compared with the conventional FMEA method, the FMEA method based on knowledge graph has
significant advantages in forming failure mode knowledge base, fault reasoning ability, fault range
analysis, and fault multi-level analysis.
Inspired by the aforementioned discussions, this paper explores a reliability analysis method
based on improved FMEA combined with machine learning for intelligent manufacturing systems.
The main contributions of the paper are as follows.
(1) the knowledge graph of failure modes of the intelligent manufacturing systems is constructed,
which counts for a great deal for the establishment of the structured network of failure modes
and knowledge base of the intelligent manufacturing systems. In addition, the knowledge
reasoning ability and knowledge retrieval ability contained in the knowledge graph have
great value of guidance and reference for evaluating and preventing failure modes.
(2) Combined with grey relation analysis (GRA) and K-means clustering, an improved FMEA
model is established. The improved model can more reasonably reflect the risk prioritization
of failure modes compared with the conventional FMEA method, and provides the theoretical
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basis for the prevention and monitoring of failure modes of complex systems such as
intelligent manufacturing systems.
The remainder of this paper is organized as follows. In Section 2, a reliability analysis model
utilizing knowledge graph theory, GRA, and machine learning is developed for prioritizing and
classifying failure modes by improving FMEA. In Section 3, The application of the improved FMEA
approach on reliability analysis of the intelligent manufacturing systems is provided. In Section 4,
the results are discussed and suggestions are made for the risk prevention and monitoring of the
intelligent manufacturing systems. Finally, concluding remarks and further research proposals are
presented in Section 5.
2 The proposed method
In this section, we propose a new reliability analysis method for FMEA based on knowledge graph,
GRA, and machine learning. The proposed method mainly consists of three phases: evaluating the
risk of failure modes, determining the risk prioritization of failure modes, and extracting the features
of failure modes. The method is detailedly described in subsequent sections.
2.1 Evaluate the risk of failure modes
In order to evaluate the risk of failure modes, it is necessary to effectively identify the potential
failure modes, determine the standard of evaluation linguistic terms, and organize an FMEA team
for evaluation. Therefore, a structured network of failure modes based on the knowledge graph is
proposed in this phase.
Step 1. Identify the potential failure modes and construct the knowledge graph
The construction process of the knowledge graph includes five key technical modules: knowledge
extraction, knowledge representation, knowledge fusion, knowledge reasoning, and knowledge
storage, which structures scattered data and integrates them into a complete knowledge base. In the
early stage, the knowledge graph of failure modes is, to a certain extent, dependent on expertsβ
subjective judgement. Consequently, experts are required to operate manually to form training sets
in information extraction, information processing, and information fusion. The architecture of the
knowledge graph of failure modes is shown in Figure 1.
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Figure 1 The construction process of knowledge graph
Step 2. Evaluate failure modes using linguistic terms
Suppose a general FMEA problem including π failure modes πΉππ(π = 1,2,β― ,π) based on π risk factors, which are evaluated by π FMEA team members π·π(π = 1,2,β― , π) . In order to reflect the relative importance of experts in the evaluation process, each team member should be assigned a weight ππ > 0(π = 1,2,β― , π) satisfying β ππ = 1ππ=1 . Experts evaluate various failure modes based on Likert's five scaling method, and established the FMEA evaluation matrix F =(πΉππ)πΓπ. The five levels of linguistic terms correspond to 1, 3, 5, 7, and 9 points respectively. In
addition, each failure mode possesses three risk factors, including S, O, and D. The linguistic terms for rating failure modes are shown in Table 1.
Table 1 Linguistic terms for rating failure modes
linguistic terms S O D
Very Low (VL) The system operates normally, and the failure
mode has little effect on the system function
Occur rarely The cause of failure mode
can be definitely detected
Low (L) The system is able to operate, and the failure
mode damages the function of a part of
system modules
Occur less The cause of failure mode
can be easily detected
Medium (M) The system operation is blocked, and the
failure mode has an impact on the key system
modules
Occur
occasionally
The difficulty of detecting
the cause of failure is
medium
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High (H) The system runs abnormally, and the failure
mode has a serious impact on the system
function
Occur
sometimes
Difficult to detect the cause
of failure
Very High (VH) The system stops running and the failure
mode damages the system
Occur
frequently
Failure causes cannot be
detected or control
measures are not designed
2.2 Determine the risk prioritization of failure modes
In this phase, the weight of risk factors is taken into consideration, and GRA is utilized to rank the
risk of failure modes. GRA is a method of multi-factor statistical analysis, which usually takes the
uncertain system as the research object. It is a method to quantitatively describe the changing trend
of the system. This method can greatly reduce the analysis difficulties caused by unclear and missing
information, and is often used to improve the ranking accuracy in FMEA.
Step 3. Calculate the weights of risk factors by AHP
The concrete procedure of the AHP method is summarized as follows.
(1) Establish the judgement matrix
Each expert of the FMEA team compares the importance of the three risk factors S, O, D, and
establishes a judgement matrix. The judgement matrix of the k-th expert is π»π = (π»ππ)πΓπ(π =1,2,β― , π), in which each pair of factors is compared using numerical rating. π»ππ represents the
relative importance of the i-th risk factor over the j-th risk factor, and π»ππ = 1π»ππ (π, π = 1,2,β― , π). (2) Calculate the consistency ratio (CR)
CR is a ratio between the matrixβs consistency index and random index, used to indicate the
probability that the matrix judgements were randomly generated [], and in general ranges from 0 to
1. A πΆπ of 0.1 or less is considered acceptable. Otherwise, the judgements are untrustworthy and
need to be reconstructed. CR is defined as: πΆπ = πΆπΌπ πΌ (1)
where RI is the random consistency index related to the dimension of matrices. Obtained by the "table look-up" method, when π = 3, π πΌ = 0.52. CI is the consistency index, and can be expressed as: πΆπΌ = ππππ₯ β ππ β 1 (2)
where ππππ₯ is the largest or principal eigenvalue of the matrix and π is the order of the matrix.
(3) Obtain the weight vector
Through normalization, the weights of factors based on the k-th expert's opinion is obtained as:
πππ = 1πβ( π§ππ,πβ π§ππ,πππ=1 )
π
π=1(3)
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Where π, π = 1,2,β― , π, and π = 1,2,β― , π. The weights of risk factors obtained by combining the opinions of experts are calculated as
ππ =βπππππππ=1 (4)
Where π = 1,2,β― , π. The weight vector of risk factors S, O, D is expressed as: π = (π1, π2, π3)π (5)
Step 4. Calculate risk score of failure modes by GRA
GRA is adopted as a tool for risk prioritization, and the specific description is given below.
(1) Set the reference sequences and the comparative sequences
As the first stage, values in the FMEA evaluation matrix for each failure mode are processed into
comparability sequences. The reference sequence which indicates the ideal state is set as π0 ={π₯0(1), π₯0(2), β¦ π₯0(n)}. The i-th failure mode can be expressed as a comparative sequence ππ ={π₯π(1), π₯π(2), β¦ π₯π(n)}, π = 1,2,β― ,π.
Besides, the matrix numbers should be normalized first by non-dimensional treatment. Because
the lower the risk priority, the more reliable the failure modes are, which is a cost criterion [29]. The
normalized equation is defined as: πππ = 1 πΉππβββ (1 πΉππβ )2ππ=1 (6)
Where π = 1,2,β― ,π and π = 1,2,β― , π.
(2) Calculating the grey relational coefficient for each failure mode
Based on the normalized matrix, the relational coefficient was constructed using the following
equation:
π0π(π) = minπ minπ |π₯0(π) β π₯π(π)| + π β maxπ maxπ |π₯0(π) β π₯π(π)||π₯0(π) β π₯π(π)| + π β maxπ maxπ |π₯0(π) β π₯π(π)| (7)
Where π = 1,2,β― ,π and π = 1,2,β― , π. This process is used for determining how close π₯π(π) is to π₯0(π). The larger the coefficients, the closer π₯π(π) and π₯0(π). Let π be the distinguishing
coefficient, ππ(0,1) and usually is set to 0.5, which affects the relative value of risk without
changing the priority.
(3) Calculating the grey relational grade
The grey relational grade can be calculated by Eq. (8). The larger the value of πΎ0π, the higher the
failure mode risk priority.
πΎ0π =βπππ0π(π)ππ=1 (8)
2.3 Extract the features of failure modes
Failure modes are caused by different causes. In addition to identifying failure modes, we hope to
carry out targeted maintenance according to different types of quality problems. Therefore, the
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classification and feature extraction of various failure modes is the basis of research. In this phase,
we combine the K-means clustering algorithm to classify failure modes, based on RPN calculated
by the conventional FMEA and the grey relational grade obtained in the previous step.
The k-means is a well-known unsupervised machine learning algorithm that solve clustering
problems. It is used for discovering the cluster structure in data sets with the greatest similarity
within the same cluster, but the greatest dissimilarity between different clusters [30]. K-means
method is often applied to the cluster analysis of scattered points in the two-dimensional coordinate
system, which is suitable for the classification of failure modes and provides a theoretical basis for
fault maintenance and continuous improvement.
Step 5. Classify failure modes by the k-means algorithm
(1) Divide failure modes into s clusters.
(2) Let U = {u1, u2,β― , uπ } be the initial centroids of the cluster by random selection.
(3) Keep iterating the following until optimal centroids are found which means the clusters will
not change anymore.
a. Calculate the sum of squared distance between data points and centroids, and assign each
data point ππ to the nearest cluster [31].
b. Re-compute the centroids for the clusters by taking the average π’π(π = 1,2,β― , π ) of all
data points of that cluster iteratively.
c. K-means terminates since the centroids converge and do not change.
According to the K-means clustering results, failure modes are divided into s categories, and the
corresponding measures for monitoring and preventing failure modes can be put forward based on
the features of different categories.
3 Implementation
In this section, the model proposed in the previous part is utilized to analyze the reliability of the
intelligent manufacturing systems, including constructing the knowledge graph and evaluating
failure modes, grey relational ranking of failure modes, and K-means clustering analysis.
Generally, the complete intelligent manufacturing systems consist of multiple subsystems, which
also belong to different system dimension layers. Referring to the intelligent manufacturing
standardization systems, a 5-tier architecture is established, consisting of the network layer,
enterprise layer, management layer, control layer, and equipment layer.
(1) The network layer refers to the data information network based on Ethernet, which can realize
the information interaction between enterprises and the data transmission and storage within
the enterprise.
(2) The enterprise layer refers to the management and operation system built by the enterprise
itself under the network layer. It is the most comprehensive and core functional layer in the
enterprise, including subsystems such as enterprise resource planning (ERP), supply chain
management (SCM), and customer the relationship management (CRM) system.
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(3) The management layer, as a connecting link between the preceding and the following layer,
realizes the transition from enterprise management to workshop production. It is mainly
composed of subsystems that control the overall production of the enterprise, including
manufacturing execution system (MES), product lifecycle management (PLM), etc.
(4) The control layer is the functional layer that realizes the production of specific workshops. It
is also one of the biggest characteristics of the intelligent manufacturing systems that
distinguishes it from the conventional manufacturing field. It includes subsystems such as
supervisory control and data acquisition (SCADA), distributed control system (DCS),
programmable logic controller (PLC), etc.
(5) The equipment layer refers to the frontline production workshop units, including a series of
intelligent production equipment, which can most intuitively reflect the intelligence and
informatization of the production process.
Based on the division of functional layers mentioned above, combined with relevant literature
and actual investigations, a total of 26 failure modes of the intelligent manufacturing systems are
determined in this paper. The specific failure modes and their causes are shown in Table 2.
Table 2 The FMEA of the intelligent manufacturing systems
Layers Symbol Failure modes Failure effects
Network
layer
πΉπ1 Malicious attacks on software systems The system is not functioning properly πΉπ2 Not effectively fused of data information Information security vulnerabilities in
the system πΉπ3 Poor interface compatibility between
systems
Inefficient system operation
πΉπ4 Lack of system scalability Unable to follow up the production plan πΉπ5 Transmission failure of the
communication line
System network paralysis
πΉπ6 Data loss during transmission and storage System operation is blocked πΉπ7 Illegally accessed and tampered with data Leakage of enterprise internal
information
Enterprise
layer
πΉπ8 Business changes on the supply side Difficult to satisfy production demands
by supply πΉπ9 Business changes on the demand side Difficult to satisfy business demands by
production πΉπ10 Logical confusion of the internal functions
of the system
Low system operation efficiency
πΉπ11 Error in the bill of materials Unable to produce effectively πΉπ12 Unreasonable production planning Unable to satisfy the delivery date πΉπ13 Inconsistent production line standards Information sharing within the
enterprise is blocked
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πΉπ14 The rupture of the internal supply chain Unable to produce normally πΉπ15 Products fail to satisfy market
expectations
The project needs to be adjusted or
terminated
Management
layer
πΉπ16 Unreasonable production scheduling Bottlenecks in the production line and
overstock πΉπ17 Insufficient staff experience and ability Technical problems occurred during
production πΉπ18 Incomplete functional testing Bugs appear in the runtime software πΉπ19 Inaccurate product quality control Product quality cannot be guaranteed
Control layer πΉπ20 Inaccurate data collection Inaccurate database data πΉπ21 Inaccurate equipment condition
monitoring
Inability to guide prediction and
maintenance πΉπ22 Poor process control Unable to make timely feedbacks and
adjustments to production activities
Equipment
layer
πΉπ23 Device execution error Equipment downtime and production
rework πΉπ24 Equipment maintenance error Unable to identify and confirm the
failures πΉπ25 Equipment aging failure Production process downtime πΉπ26 Natural disaster Endanger the hardware safety of system
equipment
In addition, in the intelligent manufacturing systems, the failure cause corresponding to each
failure mode may be caused by the comprehensive causes of different subsystems. Therefore,
combining literature and actual conditions, this paper summarizes four types of failure causes,
including human error, design defect, configuration defect, and force majeure. These four failure
causes cover most of the defects and problems that may occur in the actual production process.
Among them, force majeure includes not only some natural accidents and disasters, but also some
inevitable situations, such as changes in the relation between supply and demand. For each
enterprise, as long as the enterprise is in the macro environment of the market, the relation between
supply and demand will inevitably exist and continue to change, which is difficult to avoid and
eliminate. The classification of failure causes is shown in Table 3.
Table 3 The classification of failure causes
Failure
modes Failure causes
Classification
Human
error
Design
defect
Configuration
defect
Force
majeure πΉπ1 The software system is attacked by external
network β
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πΉπ2 The communication protocol is not unified β πΉπ3 Non-unified application interface β πΉπ4 Insufficient modules and capacity of the
system β πΉπ5 Failure on the network operator side β πΉπ6 Design and application errors of the database
management system β β πΉπ7 Design defects of database security β πΉπ8 The supplier's supply plan varies with the
macro environment β β πΉπ9 Customer demand varies with the macro
environment β πΉπ10 Unreasonable system architecture β πΉπ11 Errors caused by the delivery staff β πΉπ12 Errors caused by the arranger of production
plan β πΉπ13 Design defects of production lines β πΉπ14 Design and application errors of supply chain
management system β β πΉπ15 Unreasonable product design and the impact
of the macro environment β β πΉπ16 Errors caused by the dispatcher and
unreasonable production layout β β πΉπ17 Lack of relevant knowledge training for
employees β πΉπ18 Omissions in system test β β πΉπ19 Design and application errors of the lifecycle
management system β β πΉπ20 Operation errors of data collectors and
insufficient equipment accuracy β β πΉπ21 Insufficient accuracy and sensitivity of sensor
identification β πΉπ22 Design defects of control system β πΉπ23 Personnel operation errors β πΉπ24 Inaccurate fault diagnosis β πΉπ25 The hardware is aging gradually during
operation β β πΉπ26 Natural disasters or factory accidents β
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Using the editing tool ProtΓ©gΓ© and modeling language OWL, a computer-understandable
knowledge graph of failure modes of intelligent manufacturing systems is established and stored in
the knowledge base of failure modes, which is shown in Figure 2. Among them, the yellow line
indicates the unidirectional diffusion of the effects of failure modes, and the red line indicates the
bidirectional diffusion of the effects of failure modes. Through the knowledge graph, the location
and relevant information of each failure mode in the entire intelligent manufacturing systems can
be traced. Furthermore, the knowledge graph provides the functions of semantic search and semantic
inference. In the knowledge base, users can retrieve failure modes according to keywords and infer
the possible coupling relationship with other failure modes that are superior, subordinate, or peer.
When the knowledge base is broader and more accurate, the reasoning ability of the knowledge
graph will be stronger. The failure mode network can be used as the basis of the knowledge base
and greatly improve the efficiency of knowledge reuse.
For instance, for πΉπ21 inaccurate equipment condition monitoring, the most likely failure cause
comes from the testing equipment or the system itself. According to the knowledge graph of failure
modes, we can realize that some failure modes at the network layer, such as πΉπ5 transmission
failure of the communication line, may appear as the superior level failure modes of πΉπ21. Similarly, πΉπ21 is also the superior level failure mode of πΉπ19 inaccurate product quality control.
Figure 2 Knowledge graph of failure modes of the intelligent manufacturing systems
At present, the application of the knowledge graph in intelligent manufacturing systems still has
the resistance as follows.
(1) There is a lack of relevant databases in the field of intelligent manufacturing. Knowledge
graph technology requires a large amount of labeled data to construct training sets. However,
intelligent manufacturing is an emerging field. Due to the confidentiality of the database in
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the intelligent manufacturing field, the particularity of intelligent manufacturing enterprises,
and the lack of relevant industry standards, the databases often cannot be effectively
integrated, which is difficult to provide a good training database for the application of the
knowledge graph.
(2) The advantages of knowledge graph theory applied to intelligent manufacturing systems are
not clear enough. The knowledge graph has powerful processing capabilities for huge data
sets, which reflects its potential for applications in the field of intelligent manufacturing.
However, the issues are not clear enough that how to apply the knowledge graph technology
to the design, assembly, manufacturing, and other processes of the intelligent manufacturing
systems, and how effective it is for the process improvement of the intelligent manufacturing
systems.
The FMEA team was organized to undertake the risk evaluation, which is consisted of three
experts A, B, and C. A is an enterprise MES implementation consultant, B is an enterprise
informatization consultant, and C is an MES reliability expert in the university. In view of the
expertsβ different knowledge backgrounds and professional fields, distinct weights are allocated to
them to reflect their importance in the FMEA process, i.e. π = (0.3,0.3,0.4). The weight vector of
risk factors S, O, D is calculated using Eqs. (3)-(5), and the result is π = (0.4900,0.2698,0.2402)π
[19]. The linguistic evaluations on failure modes by the FMEA team members are shown in Table
4. The evaluation results can be transformed into a numerical FMEA evaluation matrix F =(πΉππ)26Γ3. Table 4 Linguistic evaluations on failure modes by the FMEA team members
Failure modes
A B C
S O D S O D S O D πΉπ1 VH VL L VH L M VH VL L πΉπ2 M H M M H L M H M πΉπ3 M H M H H L H H M πΉπ4 L H H M H L M H H πΉπ5 VH VL L VH L L VH VL L πΉπ6 H L H H VL H H L H πΉπ7 H L M VH L H VH L H πΉπ8 M H H H M L M H L πΉπ9 L H H H M L L H L πΉπ10 M M M H H M H M M πΉπ11 H L M M L M H L M πΉπ12 M H H M H H M H H πΉπ13 H H M H M L H H M πΉπ14 M L M H L L M L M
14
πΉπ15 H H H L M L H M H πΉπ16 H H H M M H H H H πΉπ17 M H L H M L M M L πΉπ18 M H H H H H M H H πΉπ19 M L M L L L M L M πΉπ20 H H M H H M H H M πΉπ21 L M H L H M L M H πΉπ22 M L L M H L M M L πΉπ23 H L M H L L H L M πΉπ24 M L H L L L M L M πΉπ25 M M M L M L M M M πΉπ26 VH VL L VH VL L VH VL L
Combing the linguistic evaluations in Table 4 with expert weights, The FMEA evaluation matrix
is shown in Table 5.
Table 5 The FMEA evaluation matrix of failure modes
Failure modes S O D Failure modes S O D πΉπ1 9 1.6 3.6 πΉπ14 7 6.4 4.4 πΉπ2 5 7 4.4 πΉπ15 5.6 3 4.4 πΉπ3 6.4 7 4.4 πΉπ16 5.8 5.6 5.8 πΉπ4 4.4 7 5.8 πΉπ17 6.4 6.4 7 πΉπ5 9 1.6 3 πΉπ18 5.6 5.6 3 πΉπ6 7 2.4 7 πΉπ19 5.6 7 7 πΉπ7 8.4 3 6.4 πΉπ20 4.4 3 4.4 πΉπ8 5.6 6.4 4.2 πΉπ21 7 7 5 πΉπ9 4.2 6.4 4.2 πΉπ22 3 5.6 6.4 πΉπ10 6.4 5.6 5 πΉπ23 5 5 3 πΉπ11 6.4 3 5 πΉπ24 7 3 4.4 πΉπ12 5 7 7 πΉπ25 4.4 3 5 πΉπ13 9 1.6 3.6 πΉπ26 4.4 5 4.4
The matrix is normalized using Eq. (6), for example, π11 = 1 πΉ11βββ (1 πΉπ1β )226π=1 = 0.1183
When carrying out FMEA, the smaller the risk factor value in the normalized matrix, the larger
the risk of failure mode. Thus, the failure mode reference sequence, which represents failure modes
of high risk of intelligent manufacturing systems, is consisted of the minimum value of each risk
factor in the normalized matrix. The failure mode reference sequence is set as (0.1183,0.0818,0.1231). The grey relational grades are calculated using Eq. (7)-(8). The results
15
and the comparison of risk rankings between the RPN method and the GRA method are shown in
Table 6.
Table 6 Comparison of risk rankings
Failure modes RPN Ranking Grey relational grade Ranking πΉπ1 51.8400 24 0.7800 16 πΉπ2 154.0000 11 0.8087 13 πΉπ3 197.1200 5 0.8648 6 πΉπ4 178.6400 9 0.8132 12 πΉπ5 43.2000 25 0.7609 18 πΉπ6 117.6000 13 0.8355 10 πΉπ7 161.2800 10 0.8899 3 πΉπ8 150.5280 12 0.8207 11 πΉπ9 112.8960 14 0.7576 19 πΉπ10 179.2000 8 0.8589 8 πΉπ11 96.0000 17 0.7966 15 πΉπ12 245.0000 3 0.8636 7 πΉπ13 197.1200 5 0.8776 5 πΉπ14 73.9200 21 0.7511 20 πΉπ15 188.3840 7 0.8537 9 πΉπ16 286.7200 1 0.9116 1 πΉπ17 94.0800 18 0.7720 17 πΉπ18 274.4000 2 0.8891 4 πΉπ19 58.0800 23 0.6979 26 πΉπ20 245.0000 3 0.9005 2 πΉπ21 107.5200 15 0.7279 23 πΉπ22 75.0000 20 0.7356 22 πΉπ23 92.4000 19 0.8028 14 πΉπ24 66.0000 22 0.7127 25 πΉπ25 96.8000 16 0.7492 21 πΉπ26 27.0000 26 0.7239 24
In practice, fault modes are generally divided into three categories, including fault modes with
great impact, general impact, and little impact. Therefore, let s = 3. On the basis of the above data,
through K-means clustering analysis by MATLAB programming, the clustering results are obtained
and shown in Figure 3 and Table 7.
16
Figure 3 The results of K-means clustering
As shown in Figure 3, 26 failure modes are divided into three clusters. The cluster 1 marked in
blue includes πΉπ1 , πΉπ5 , πΉπ9 , πΉπ14 , πΉπ17 , πΉπ19 , πΉπ21 , πΉπ22 , πΉπ24 , πΉπ25 , and πΉπ26 . The cluster 2 marked in red includes πΉπ2 , πΉπ4 , πΉπ6 , πΉπ8 , πΉπ11 , and πΉπ23 . The cluster 3
marked in yellow includes πΉπ3 , πΉπ7 , πΉπ10 , πΉπ12 , πΉπ13 , πΉπ15 , πΉπ16 , πΉπ18 , and πΉπ20 .
After communication and discussion with experts, it is considered that the clustering results are in
line with the actual situation and reasonable.
Table 7 K-means clustering of failure modes
Clusters Failure
modes
Ranking
of RPN
Ranking of Grey
relational grade
Clusters Failure
modes
Ranking
of RPN
Ranking of Grey
relational grade
Cluster 1 πΉπ1 24 16 Cluster 2 πΉπ2 11 13 πΉπ5 25 18 πΉπ3 5 6 πΉπ9 14 19 πΉπ4 9 12 πΉπ11 17 15 πΉπ6 13 10 πΉπ14 21 20 πΉπ7 10 3 πΉπ17 18 17 πΉπ8 12 11 πΉπ19 23 26 πΉπ10 8 8 πΉπ21 15 23 πΉπ13 5 5 πΉπ22 20 22 πΉπ15 7 9 πΉπ23 19 14 Cluster 3 πΉπ12 3 7
17
πΉπ24 22 25 πΉπ16 1 1 πΉπ25 16 21 πΉπ18 2 4 πΉπ26 26 24 πΉπ20 3 2
4 Discussion and suggestions
4.1 Comparison and discussion
According to Table 6, the comparison of failure mode ranking between the conventional FMEA
method and the improved FMEA method is shown in Figure 4. It can be obtained that the RPN
calculated by the conventional FMEA method has duplicate values. For example, the risk priority
of πΉπ12 and πΉπ20 are both ranked third, the risk priority of πΉπ3 and πΉπ13 are both ranked
fifth, and it is difficult to judge the specific risk priority of the failure mode and give suggestions on
reliability management. This is because the conventional FMEA method does not consider the
weight of risk factors, and the calculation method of RPN also determines that it is easy to cause
the repetition of RPN. However, the improved FMEA method solves these problems well.
Figure 4 Comparison of failure mode ranking between the conventional FMEA method and
the improved FMEA method
From the perspective of risk priority, the cluster 1 contains the failure modes with low values of
RPN and grey relational grades, that is, whether in the conventional FMEA method or the improved
FMEA method, the risk priority of these failure modes is low. From the perspective of risk factors,
on the whole, the probability of occurrence and detection of failure modes in cluster 1 is relatively
low. From the perspective of the structured network of failure modes, the failure modes in cluster 1
are widely distributed in all functional layers of the intelligent manufacturing systems, and mainly
distributed in the downstream functional layers. From the perspective of risk causes, the failure
modes in cluster 1 are mostly caused by equipment failures and network failures, and most of them
contain the factors of human error. For the intelligent manufacturing systems, human error is
0
5
10
15
20
25
30
FM
1
FM
2
FM
3
FM
4
FM
5
FM
6
FM
7
FM
8
FM
9
FM
10
FM
11
FM
12
FM
13
FM
14
FM
15
FM
16
FM
17
FM
18
FM
19
FM
20
FM
21
FM
22
FM
23
FM
24
FM
25
FM
26
Conventional FMEA Proposed method
18
avoidable. With the dynamic development of the intelligent manufacturing systems, human error
will show a decreasing trend. The only exception is F26 which is only caused by force majeure.
Although its severity is high, its occurrence and detection are low, and the consequences can be
easily controlled. In general, this type of failure mode has a relatively small impact on the reliability
of intelligent manufacturing systems.
In cluster 2, from the perspective of risk priority, the ranking of RPN and grey relational grade
are both at the medium level, or one value is larger and the other is smaller. From the perspective of
risk factors, the probability of severity (S) of failure modes in cluster 2 is relatively high. From the
perspective of the structured network of failure modes, the failure modes in cluster 2 are basically
in the upstream position of the failure mode network. They not only affect their own functional layer,
but also affect their downstream functional layers, causing the overall function loss and downtime
of the intelligent manufacturing systems. This is difficult to be reflected in the conventional FMEA
method, and is the main reason for the difference in risk priority between the conventional FMEA
method and the improved FMEA method.
From the perspective of risk priority, the cluster 3 contains the failure modes with high values of
RPN and grey relational grades. From the perspective of risk factors, the failure modes in cluster 3
have high values of S, O, and D, indicating that they have a strong impact on the intelligent
manufacturing systems and need to invest more resources for prevention and monitoring.
In conclusion, the improved FMEA method can reflect the characteristic that the impact degree
of failure modes will dynamically change with the development of the intelligent manufacturing
systems. In addition, the improved FMEA method can rank the failure modes that affect other
functional layers in a higher risk priority and then cause subsequent failures. The improved FMEA
method is more suitable for the reliability analysis of the intelligent manufacturing systems than the
conventional FMEA method, and is more reasonable and in line with reality.
4.2 Preventive and monitoring measures
4.2.1 Failure modes in cluster 1
The risk priority of failure modes in cluster 1 is not high, and the failure modes occur less frequently
in the intelligent manufacturing systems, and they can be detected in time and a series of control
measures can be taken. Such failure modes are often in the scope of the existing monitoring system,
and have a relatively sound prevention mechanism. For enterprises, they only need to improve the
existing prevention and control mechanism, and there is no need to invest additional resources to
prevent and control failure modes in cluster 1.
Among them, the failure causes of πΉπ11, πΉπ14, πΉπ17, πΉπ19, πΉπ23 and πΉπ24 all include
human error. Therefore, it is necessary to strengthen the process review mechanism and the relevant
business training of employees in various departments, and adopt a more comprehensive staff
management mechanism. For the intelligent manufacturing systems, the management level and
operation level are bound to continue to improve, the requirements for relevant personnel will also
19
increase, and the severity, occurrence, and detection of such failure modes will continue to decline.
In addition, πΉπ1, πΉπ5, πΉπ9, πΉπ21, πΉπ22 and πΉπ25 are mainly caused by equipment failures
and network failures. It is necessary to improve the software and hardware of the intelligent
manufacturing systems, which can greatly reduce the occurrence of failure modes in cluster 1, and
is the inevitable trend of the development of the intelligent manufacturing systems. Since πΉπ26 (natural disaster) is uncontrollable and difficult to predict, intelligent manufacturing enterprises
need to formulate complete emergency plans, and always pay attention to relevant early warning
information, so that the emergency plans can be activated before the disaster strikes, and the failure
modes can be restored in an orderly manner to ensure production.
4.2.2 Failure modes in cluster 2
A significant common feature of failure modes in cluster 2 is that they are at the upstream of the
structured network of failure modes. This type of failure mode often leads to the occurrence of a
series of subsequent failure modes and has a serious impact on the integrity of the intelligent
manufacturing systems. The suggestion for such failure modes is to focus more on prevention and
control, carry out feedback treatment as soon as possible, and minimize the impact in time before
the failure modes radiate to the whole system. Therefore, it is necessary to establish a complete
predictive monitoring system and a maintenance system that can respond to such failure modes in a
timely manner. In the future, when the field of intelligent manufacturing is becoming more and more
mature, this type of failure mode will gradually be transferred to cluster 1, which are controllable
risks that the intelligent manufacturing systems can carry out automated supervision.
Among them, πΉπ2, πΉπ3, πΉπ4, πΉπ10 and πΉπ13 are all caused by system design defects.
It is necessary to optimize the relevant system design and improve the control system, which will
help enterprises to minimize the impact of failure modes in time. πΉπ6 and πΉπ7 are related to
data security, and the impact can be reduced by establishing a complete information security
network architecture, which is necessary for functional intelligent manufacturing systems. πΉπ8
and πΉπ15 are affected by the macro environment of the market. The characteristics of real-time
changes in the market will make these two failure modes unavoidable and difficult to predict, so it
is difficult to establish a monitoring mechanism. Then the better strategy is to enhance the flexibility
and toughness of the enterprise itself, so that the enterprise can make corresponding production
adjustments in time to adapt to the changes of market supply and demand.
4.2.3 Failure modes in cluster 3
Failure modes in cluster 3 have the greatest impact on intelligent manufacturing systems, and
enterprises should invest the most resources to prevent. The intelligent manufacturing systems
contain a huge number and variety of related equipment. Each equipment is closely coordinated and
operated. Once a certain link goes wrong, it will cause the current production stagnation, and at
worst, affect the function of the whole intelligent manufacturing systems. πΉπ12, πΉπ16, πΉπ18
and πΉπ20 all involve important links in the internal operation of the intelligent manufacturing
20
systems. For these four failure modes, it is necessary to establish an early warning and monitoring
system to find failures in time, and formulate a complete feedback mechanism. When a fault occurs,
it shall be maintained in time to restore its function before irreparable losses are caused, so as to
ensure the continuous operation of the whole intelligent manufacturing systems.
5 Conclusion
Taking the transformative development of the manufacturing field as an entry point, an improved
FMEA method is proposed, combined with the effective method of machine learning in the field of
data processing, and applied to the reliability analysis of the intelligent manufacturing systems.
In this study, we summarize the failure modes into three clusters. The results show that for the
intelligent manufacturing systems, failure modes in cluster 1 have low risk priority, which is mostly
caused by equipment failures and network failures, and most of them contain the factors of human
error which will show a decreasing trend with the dynamic development of the intelligent
manufacturing systems. For enterprises, they only need to improve the existing prevention and
control mechanism, and there is no need to invest additional resources for reliability management.
Failure modes in cluster 2 have a high severity index, which is often in the upstream position of the
failure mode network, and its occurrence will radiate to the downstream functional layers, resulting
in the loss of the overall function and downtime of the intelligent manufacturing systems. With the
maturity of the intelligent manufacturing systems, this type of failure mode will gradually be
transferred to cluster 1, which are controllable risks that the intelligent manufacturing systems can
carry out automated supervision. Failure modes in cluster 3 involve important links in the internal
operation of the intelligent manufacturing systems, which have a strong impact on the intelligent
manufacturing systems and needs to invest more resources for prevention and monitoring.
Compared with the conventional model, the improved FMEA can reflect the characteristic that
the impact degree of failure modes will dynamically change with the development of the intelligent
manufacturing systems. Moreover, the improved FMEA put the failure modes that are able to
radiate other functional layers in a higher risk prioritization, which is more in line with reality. In
addition, the greatest advantage of machine learning lies in its ability to process huge and complex
databases. Despite the knowledge base of failure modes of intelligent manufacturing established
in this paper is small, a complete structured network will be formed and the improved FMEA
method can bring its superiority of data processing into full play when the knowledge base gets
wider and wider.
In summary, the proposed method is effective and robust for the reliability management of
complex systems, including intelligent manufacturing systems. In future researches, we can further
consider how to build the knowledge base that can describe more complex relationships between
failure modes, and establish the more scientific classification logic of failure modes in reliability
analysis.
21
6 Declaration
Acknowledgements
Not applicable.
Authorsβ contributions
CD was in charge of the whole trial, conceptualization, methodology, writing-reviewing, and editing;
MZ wrote the original manuscript and prepared the methodology; KW assisted with data curation,
visualization, investigation, and analyses. WZ reviewed and checked the manuscript. All authors
read and approved the final manuscript.
Authors' information
Chunyan Duan, born in 1987, is currently an assistant professor at School of Mechanical
Engineering, Tongji University, China. Her main research interests include intelligent
manufacturing and reliability analysis.
Mengshan Zhu, born in 1997, is currently a PhD candidate at School of Economics and Management,
Tongji University, China. Her main research interests include intelligent manufacturing and
reliability analysis.
Kangfan Wang, born in 1998, is currently an employee at China Railway Construction Engineering
Group. He received his bachelor degree from Tongji University, China in 2021. His main research
interests include intelligent manufacturing and reliability analysis.
Wenyong Zhou, born in 1969, is currently a professor and a PhD candidate supervisor at School of
Economics and Management, Tongji University, China. His main research interests include quality
management and reliability analysis.
Funding
Supported by Shanghai Pujiang Program (Grant No. 20PJ1413700).
Availability of data and materials
The datasets supporting the conclusions of this article are included within the article.
Competing interests
The authors declare no competing financial interests.
22
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