predictive maintenance on azure iot -...

Predictive Maintenanceon Azure IoTIoT와 Big Data 를 활용한 예지 정비 시스템 구축

In Kee Paek, Cloud Solution Architect

IoT 기반 predictive maintenance

를 통해 장비의 다운타임을 50%1

까지 절감할 수 있음

Predictive maintenance 적용으로

설비 종합 효율(OEE)을 30%3

까지 증대 시킬 수 있음

다운타임 최소화를 통해 제조사는

연간 약 $630B 의 잠재적 수익

효과를 볼 것으로 예측됨 (2025년기준)

특정 비즈니스에서는, 계획된 유지보수 작업이 계획되지 않은 수리

작업에 비해 10배이상 비용이

절약될 수 있음

1 McKinsey, The Internet of Things: Mapping the Value Beyond the Hype, 2015

2 McKinsey, The Internet of Things: Mapping the Value Beyond the Hype, 2015

3 GE, The Impact of No Unplanned Downtime, 2014

4 GE, The Impact of No Unplanned Downtime, 2014

“In contrast to a traditional preventive maintenance system, predictive maintenance solutions enables customers to strategically plan their maintenance tasks and group them in a way that allows them to perform the required maintenance

more efficiently…This leads to even more dramatic savings in terms of labor and maintenance costs” – Melissa Topp, Director of Global Marketing, ICONICS

Address business needs across a range of scenarios…

가동시간(uptime) 최적화 및장비 기대 수명의 연장을통해 제품의 품질 개선

Gain visibility into product performance and enable workflows to respond to changing conditions.

43.1° | 35.2°

91.6° | 87.3°

MAINTENANCE INITIATED

고객 맞춤의 선도적인 서비스대응을 통해 고객 관계 및충성도 강화

Offer predictive and proactive services to address customer and product needs.

데이터 기반의 혁신적이고차별화된비즈니스 모델 개발및 기존 모델의 개선

Identify trends and potential growth opportunities using customer sentiment and product usage data.

ADD BESTPRACTICE

절감된 비용을 제품디자인이나서비스의 개선에투자

Utilize service and performance data to proactively detect product failure.

실시간 제조 공정에 예지분석 기술을효과적으로 적용한 사례

Jabil

“We’ll be able to improve our efficiencies, cut costs, and decrease our lead times,

which tie directly to our customers’ requirement to increase flexibility.”

Matt Behringer

CIO, Enterprise Operations and Quality Systems, Jabil

구현 목적Jabil wanted to better predict

errors or failures on the

assembly floor before they

occur, saving customers’ time

and money.

실행 전략Jabil was able to

transform their

manufacturing production

lines with advanced

analytics solutions built

on Microsoft technology.

적용 성과• Predicted machine processes

that will slow down or fail with

an 80% accuracy

• Reduced costs of scrap and re-

work by 17%

• Delivered energy savings of 10%

As-Is 이슈및문제

• Reduced manufacturing cycle time

• Higher cost of wasted materials, time and resources

• Inability to address customers’ critical requirement for speed to market

Product

quality not

acceptable

Jabil 이당면한비즈니스과제

• Continuous requirement to increase yield, reduce amount of scrap and re-work

• Traditional inspection techniques for ensuring quality quickly becoming

outdated

with more one-off production runs

• Adding more equipment and people to existing manufacturing processes would

not have significant impact on increasing throughput

Inspection steps along the SMT line cannot always detect the quality issues

Source of failure can be introduced at multiple stages but cannot be detected until it ispowered-up for testing at the end

디지털 기반 예지정비 역량 강화로 제품 품질 개선 실현

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적용결과

• Predicted machine processes that will slow down

or fail with an 80% accuracy

• Reduced costs of scrap and re-work by 17%

• Delivered energy savings of 10%

Variation: 11% (tolerance ≤ 11%)

Vibration frequency: Too high

Bit wear: High

Result in plant 2: Failed

PREDICTED

FAILURERecommended maintenance

in next 48 hours

PREVENTATIVE

MAINTENANCE

FOR TOMORROW

10 TuesdayOctober

7:00 AM

실행전략

Jabil was able to transform their manufacturing

production lines with advanced analytics solutions

built on Microsoft technology

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구현목적

Jabil wanted to better predict errors or failures on

the assembly floor before they occur, saving

customers’ time and money

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PREDICTED FAILURE

IN NEXT 48 HOURS

Supplier data

Customer

data

Production

data

Historical data

Azure

Services Our rate of rejection has decreased dramatically now

that we can predict these failures early in the process

디지털 기반 예지정비 역량 강화로 제품 품질 개선 실현

예지 정비 도입으로 글로벌 현장의엘리베이터 운영 역량 극대화

ThyssenKrupp Elevator

“We wanted to go beyond the industry standard of preventative maintenance,

to offer predictive and even preemptive maintenance.”

Andreas Schierenbeck

CEO, ThyssenKrupp Elevator

구현 목적ThyssenKrupp wanted to

better compete in their

industry by offering

dramatically increased

uptime, taking preventative

maintenance a step further to

predictive and even

preemptive service.

실행 전략Microsoft technology

enabled ThyssenKrupp to

monitor products via a

real-time dashboard and

instruct technicians on

optimal maintenance

activities through

dynamic predictive

models.

적용 성과• Increased elevator uptime

• Reduced costs for ThyssenKrupp

and its customers

• Developed real-time data

visualization and awareness of

issues

현장 기술자에 예지분석 기반의 정비 가이드를 적시에 제공

구현목적Thyssenkrupp wanted to better monitor their more

than 1.1 million elevators worldwide. Lack of insight

led to downtime and unpredicted failures in some

of the world’s most famous buildings

수행전략They connected thousands of sensors embedded in

their elevators to the cloud to monitor real-time

performance and proactively address issues with

Microsoft technology

적용성과

• Reduced costs for thyssenkrupp and its customers

• Increased reliability through predictive maintenance

and rapid, remote diagnostic capabilities

Maintenance in progress

Scheduled Maintenance

• Hydraulic/Passenger Elevator

• Replace brake shoes

• Check AC motor for health

!

!NEXT STOP

Microsoft Offers Two Approaches to IoT Solutions

PaaS – Azure IoT Suite

SaaS – Microsoft IoT Central

PaaS

SaaS

Microsoft Offers Two Approaches to IoT Solutions

Azure IoT Suite Microsoft IoT Central

Primary usageTo accelerate development of a custom IoT solution

that needs maximum flexibility

To accelerate time to market for straightforward IoT solutions

that don’t require deep service customization

Access to underlying

PaaS Services

Access to the underlying Azure services to

manage them, or replace them as needed.

SaaS. Fully managed solution,

underlying services aren't exposed.

FlexibilityHigh. The code for the microservices is open

source to be modified.

Medium. leverage built-in browser based user experience to cust

omize the solution model and aspects of the UI.

Skill level

Medium-High

Java or .NET skills are required to customize the solution back end.

JavaScript are required to customize the visualization.

Low

Modeling skills are required to customize the solution.

No coding skills are required.

Get started experiencePreconfigured solutions implement common

IoT scenarios. Can be deployed in minutes.Templates provide pre-built models.

Can be deployed in minutes.

Pricing You can fine-tune the services to control the cost. Simple, predictable pricing structure.

Azure IoT Suite - Accelerate Time to Value!

Modify existing rules and alerts

Fine-tuned to specific assets and processes

Integrate with back-end systems

Highly visual for your real-time operational data

Get started in minutes

Add your devices and begin tailor to your needs

Azure IoT Suite Solutions

What you get with the preconfigured solution?

Azure IoT Suite Predictive Maintenance, Remote Monitoring

Existing

Business

Process

ERP/CRM

Devices

Azure IoT SDK

(OSS)

Linux, RTOS, mBed,

Windows,

Android, iOS

Event Hub

Storage blobs DocumentDB

Web/

Mobile App

Stream

Analytics

Logic Apps

Azure

Active Directory

IoT Hub Web Jobs

Power BI

Machine Learning

Azure IoT Suite Reference Architecture

Cloud Gateway

Web App

Strea Analytics

(Rules/Insights)

IoT Edge(s)

IoT Device(s)

User

Management

Machine

LearningCold Path

StoreWarm Data

Config. &

Rules

Definition

Data Xformation

Microservices

Notifications /

Actions

Orchestration

Stream Analytics

(Rules/Insights)

AAD

OAuth2 Providers

Logic Apps

Event Grid

Azure Storage

Cosmos DB

Azure MLTSI

IoTHub

App Service

Kubernetes

Service Fabric

Spark

AF

ASA

Optional

Tech Options

Predictive Maintenance project 수행 절차

잠재적 이슈와 고장이 발생하기 전 자동 감지를 효과적으로 수행하기 위한 가이드

Azure IoT Suite solutions come with pre-built sample scenarios that include:

• Background information on the business need and objectives

• Simulated devices and sample data

• Pre-set rules and alerts, pre-defined dashboards and more

Identify the

target outcome

Inventory data

sources

Capture &

combine data

Model, test and

integrate

Integrate into

operations

1 2 3 4 5

Approach: Checklist for Machine Learning

Question is sharp.

Data measures what you care about.

Data is connected.

Data is accurate.

A lot of data.

The better the raw materials, the better the product.

E.g. Predict whether component X will fail in the next Y days

E.g. Identifiers at the level they are predicting

E.g. Will be difficult to predict failure accurately with few examples

E.g. Failures are really failures, human labels on root causes

E.g. Machine information linkable to usage information

Approach: Data Science Process

Define Objective

Data Sources

Explore Data

Machine Learning

Analysis Dataset Publish

Approach: Data Sources for Predictive Modeling

The failure history of a machine or

component within the machine.

The repair history of a machine, e.g.

previous maintenance records,

components replaced, maintenance

activities performed.

The operating characteristics of a

machine, e.g. data collected from

sensors.

FAILURE HISTORY REPAIR HISTORY MACHINE CONDITIONS

The features of machine or

components, e.g. production date,

technical specifications.

Environmental features that may

influence a machine’s performance,

e.g. location, temperature, other

interactions.

The attributes of the operator who

uses the machine, e.g. driver.

MACHINE FEATURES OPERATING CONDITIONS OPERATOR ATTRIBUTES

Approach: Predictive Modeling Techniques

Predict failures within a future period of time

BINARY CLASSIFICATION

Predict failures with their causes within a future

time period.

Predict remaining useful life within ranges of future

periods

MULTICLASS CLASSIFICATION

Predict remaining useful life, the amount of time

before the next failure

REGRESSION or SURVIVAL ANALYSIS

Identify change in normal trends to find anomalies

ANOMALY DETECTION

Predictive Maintenance framework

1Identify the target outcomeDetermine what outcome you ultimately want to achieve. In this case, we want to know how long until any given AC unit fails.

I want to understand how much time each

AC unit has left before it needs maintenance

so we can prevent unplanned failures.

Last time an AC unit failed, it cost

thousands of dollars and operations

were down for days.

AC unit

out of order


2Inventory data sourcesIdentify all potential sources of data, including the types of data and the amounts available. The outcome you are seeking will influence what data is essential and what’s optional.

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Performance

data

Maintenance logs

Weather data Failure logs

Sensor data

Include data from a variety

of sources – you may be

surprised about the places

where key information can

come from.

3Capture and combine dataConnect all of your data to a single place and prepare it for analysis.

Lay the groundwork for

a robust predictive model

by pulling in data that

includes both expected

behavior and failure logs.


4Model, test, and iterate

Identify unexpected patterns by developing statistical models using advanced analytics techniques. Stank-rank models to determine which model is best at forecasting the timing of AC unit failures.

Make your model

actionable by understanding

how much advance notice

the maintenance team needs

in order to respond.

▪ Model A

▪ Model B

▪ Model C


5Validate model using your latest data

Apply your model to live, streaming data and observe how it works in real-world conditions.

Use machine learning to refine your model and ready it for full implementation.

Be willing to refine your

approach based on the

data you gather during

the real-world pilot.

100101011000

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101000110011


6Integrate into operationsOperationalize the model by adjusting maintenance processes, systems and resources to act on near-real-time data. Make ongoing improvements by gaining insights from machine learning and advanced analytics.

Strengthen your processes

and procedures to take

advantage of what you learn.

3 weeks until failure: Order

replacement part

2 weeks until failure: Send

repair team


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