broc rovsing - open predictor - cbm

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OPEN predictorTM

INTRODUCTION TO THE OPENpredictorCONDITION AND PERFORMANCE MONITORINGTM

SYSTEMS

OPENpredictor CONDITION AND PERFORMANCE MONITORING SYSTEMS

Automatic fault diagnosis and health predictionTo assess the mechanical and functional health of plant assets, the widest range of potential machinery problems shall automatically be identified. During different transient and stationary operational states of the machines, the development of specific faults shall be identified. As the development of faults is related to machine operation, the measured data has to be classified to transient and operational states in order to achieve meaningful data comparison, automatic fault diagnosis (AutoDiagnosis) and health prediction. In the following overview, several machinery components, potential problems and their early diagnosis are presented. The OPENpredictor system can however be configured to identify a wide range of other machine specific problems when more detailed machinery information is provided. Machine components The overall health of a machine depends on the condition of all components critical for the machine operation and all dynamic forces acting on the components. The components are subdivided into rotating and stationary items such as: Mechanical and functional health assessmentROTATING COMPONENTS Rotor/Shaft Gear wheels Blades Couplings STATIONARY COMPONENTS Foundation Casing Seals Bearings Combustor

FAULTS ON ROTATING COMPONENTS Rotor/shaft: Unbalance Bent rotor Eccentricity Oil whirl Steam whirl Rubbing in bearings & seals Blades & impellers: Rubbing Cavitation surge Stall Rolling element bearings: Outer race defects Inner race defects Cage defects Lubrication deficiency Journal bearings: Shaft lift fault Wear Thrust bearings: Wear Couplings: Locking Gear wheels: Wear Back lash Tooth defects Pitting

FAULTS ON STATIONARY COMPONENTS Foundation: Looseness Casing: Electrical excitation Misalignment Thermal uneven expansion Blocked bearing movement Combustor: Resonance Flame instability Flame distribution

For specific machinery design, additional potential faults can be specified. Monitoring of e.g. gas turbine performance degradation Gas turbine performance degrades over time due to recoverable and non-recoverable mechanical changes. The result is a reduction of the power output and an increase of the heat rate (a decrease of thermal efficiency). Typical recoverable losses are fouling of blades (mostly compressor blades) and air filters. These losses can be recovered by operational cleaning procedures or by external maintenance. Typical non-recoverable losses are blade erosion or deviations in tip clearance and seals. These losses

When no design and/or installation problems exist, and then the typical sources of machine health deterioration are component fatigue, wear, erosion and external factors. Machine mechanical health monitoring provides information to optimize machine availability, while machine functional health assessment provides information with regards to machine performance (e.g. component efficiency). Monitoring problems that influence machine availability The following list is an overview of the most common faults to be monitored in order to assess the mechanical machine health:

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can only be recovered by replacement of internal gas turbine components. To monitor the deviation from a new gas turbine performance, is valuable to schedule compressor cleaning. The cleaning and filter exchange date can be forecasted and performed on the most economical date. Additionally, the long-term retrofitting can be economically optimized. The performance monitoring system uses existing process measurements such as temperatures, pressures, fuel specification, ambient conditions, etc. The system provides actual and corrected (corrected to reference ambient) estimates on: Power output Heat rate, thermal efficiency Compressor efficiency Turbine efficiency Fouling indices for compressor and filter Process problems that influence machine availability Potential dangerous process circumstances can cause machine damage such as: Cavitation Surge Stall Combustion pulsation When these problems occur, the operation shall be changed in order to avoid strong dynamic forces to blades, impellers and seals. The result is reduced operational risk and longer service life.

(residual lifetime of concerned components). The following illustrates some of the signatures used in OPENpredictor together with their main fault identification capabilities.

The Constant Percentage Bandwidth Signature (CPB) gives a general health overview of the majority of mechanical fault characteristics, such as, unbalance, misalignment, foundation looseness. It is however, specifically sensitive in identifying faults such as rolling element bearing faults, cavitation, combustor resonance and gas leaks.

OPENpredictor machine health assessment methodologyEarly fault identification OPENpredictor provides a unique library of signatures dedicated to detect and identify most mechanical problems, which may be encountered for common rotating and reciprocating machinery. The dedicated signatures check for different fault characteristics and provide selective information regarding fault symptoms, fault development as well as fault locations. Information provided by the different signatures form the basis for automatic fault diagnosis (AutoDiagnosis) and prediction of fault development

The Autospectrum Signature (FFT) is used to identify faults which can only be diagnosed with a high frequency resolution, such as electrical excitation, distinction between electrical and mechanical imbalance, blade passing excitation and system resonance.

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The Cepstrum Signature (CEP) provides a good selectivity to identify gear wheel faults in complex gearboxes and any other faults, which result in a signal with modulation character.

The Selective Envelope Detection Signature (SED) is specifically sensitive to identify faults where symptoms are of repetitive impulse character, such as rolling element bearing faults and cavitation. The AutoDiagnosisTM function uses the repetition rate of the fault to conclude the origin of the fault.

The Transient Signature (TRT) is used to automatically identify changes in the run-up or coast-down behavior of a machine. Changed critical frequencies and decreased damping are automatically diagnosed.

Vector Analysis is used extensively in the AutoDiagnosis models. OPENpredictor provides a unique implementation of Order Tracking Analysis (OTA) that provides accurate vector values, both in steady state and transient conditions. A vector consists of both an amplitude and phase value, referencing to the position of the shaft at the tacho probe. The unique algorithm provides the vector values for four harmonic components and the sub-harmonic representing shaft instability.

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The Circular Analysis Plot (CA) is used to monitor changes in symmetry for exhaust gas temperatures, thrust bearing pressure and temperature.

The Shaft Centre Line Plot (SCL) shows the location of the shaft centre in the bearing clearance. The acceptance location domain is indicated by the yellow/red alarm limits.

Modulation spectrogram as basis for SMD-function.

SMD function showing synchronous modulation strength as function of time.

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Classification of signature processing Machine behavior (and consequently measurement performed on the machine) depends on how a machine operates. Therefore, calculated signatures will also vary with machine operation. Machine health assessment has to be performed under comparable operating conditions and fault symptoms shall be assessed only within a given machine state. That is accomplished by OPENpredictor and this concept is referred to as "Classification of Signature Processing, Fault Diagnosis and Prediction".

It is important to note that a number of faults will only reveal themselves in some or even in one machine state and that some faults will only reveal themselves during transient states, such as run-up/coast-down. In conclusion, Classification is essential to improve identification sensitivity, to create realistic prediction and to avoid false warnings.

Typical start-up behaviour of a gas turbineLevel

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Time

Steady States Transient States Gradient States Calibration

Typical Transient and Steady States:

Test phase Slow roll Ventilation Run-up, cold, warm, hot Over speed testing Syncronisation to mains Low load Medium load .............

The time a machine is not producing is defined as . Production states are defined as .TRANSIENT STATES State 1 Calibration State 2 Slow roll State 3 Ventilation, temperature setting State 4 Run-up (cold and/or hot) State 5 Over speed testing State 6 Synchronization Etc. OPERATIONAL STATES State 7 Low load, inductive operation State 8 Medium load, inductive operation State 9 High load, inductive operation State 7b Low load, capacitive operation State 8b Medium load, capacity operation State 9b High load, capacity operation Etc.

Automatic fault diagnosis (AutoDiagnosis) OPENpredictor provides automatic fault diagnosis for faults in critical machine components and potential faults. The foundation of AutoDiagnosis is the synergy between classification, normalization and fault selective signatures. The result is early fault detection and accurate diagnosis with diagnostic messages presented to users in clear text. Locally defined recommendations for action, together with prediction of fault development as well as date for required inspection/maintenance ease the tasks for the users. Fault prediction pro