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研究生： Widagdo Purbowaskito
Widagdo Purbowaskito
論文名稱： 用於感應馬達預知保養之資料驅使模型式異常診斷
Data-driven Model-based Fault Diagnosis for the Predictive Maintenance of Induction Motors
指導教授： 藍振洋
Chen-yang Lan
口試委員: 黃安橋
An-Chyau Huang
林紀穎
Chi-Ying Lin
劉孟昆
Meng-Kun Liu
林峻永
Chun-Yeon Lin
陳韋任
Wei-Jen Chen
學位類別： 博士
Doctor
系所名稱： 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年： 2022
畢業學年度： 110
語文別： 英文
論文頁數： 132
外文關鍵詞： fault frequency, model-based diagnosis, state-space realization, subspace identification
相關次數： 點閱：347  下載：1 
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    • Three-phase induction motors (IMs) play a significant role as actuators in many industrial processes and facilities because they efficiently convert electrical energy into mechanical energy.
    Because they also have rugged and robust constructions, they can be operated and installed, ranging from general to highly pressured and hazardous locations.
    Nevertheless, despite the advantage in their construction, the IMs are still subject to faults and failures due to their heavy-duty operation conditions, industrial environmental stresses, and aging.
    Faults and failures in IM operation may lead to severe safety issues, costly downtime and maintenance, and energy wasting.
    Thus, maintaining the IM condition appropriate becomes vital to keep it running safely and efficiently.
    The fault diagnosis technology serves the primary role in predictive maintenance as it becomes the first method used to understand the condition of the IM during its operation.

    Signal-based fault diagnosis, such as motor current signature analysis (MCSA), provides low cost and low requirements for its implementation.
    The fact that it utilizes the simple fast Fourier-transform (FFT) algorithm makes it promising for practical implementation.
    Different fault frequency indicators can be observed once the FFT algorithm is applied to the current signals.
    These fault frequencies have been standardized in the ISO-20958 document.
    However, locating these fault frequencies in the current spectrum is challenging because of their low amplitude/low energy.
    Often, the energy of the current harmonics dominates their presence, and the spectral leakage during the current data acquisition (DAQ) process conceals them.
    MCSA fault diagnosis faces these challenges in its practical implementation.

    This study aims to address the challenges mentioned above by introducing the model-based diagnosis to substitute for the MCSA.
    The model-based diagnosis mitigates the current harmonics and other fault-unrelated frequencies from the spectrum using the sinusoidal waveform superposition principle.
    The model-based approach utilizes measured voltage signals and an IM state-space model to generate the estimated current signals.
    The estimated current and the original current signals have the same frequency components coming from the voltage signals, except the original current has other frequency components, which are the fault frequencies.
    The fault frequencies are occurred due to faults and do not come from the voltage signals.
    The destructive sinusoidal waveform superposition happens when the current estimate subtracts the original current.
    It eliminates all the same frequency components and leaves the fault frequencies in the spectrum.
    Because the domination from current harmonics and fault-unrelated frequencies has been mitigated, tracking the fault frequencies becomes effortless.
    The output of mentioned subtraction process is called the residual signal.
    Therefore, it can be hypothetically stated that the residual signals are fault-sensitive compared to the original current signals.
    The residual signals are valuable data to train a machine learning classifier for fault diagnosis.
    The fault frequencies are extracted from the residual spectrum to generate certain features for machine learning classifier training.
    The fault frequencies have the physical meaning corresponding to the faults.
    Thus, it allows them to provide valuable information for the classifier to learn during the training process.

    The proposed method is validated in an actual wastewater centrifugal pump driven by an IM in an industrial facility.
    It is to verify the proposed method's performance in actual operational conditions.
    The experiments for both single- and multiple-fault faults are conducted assuming that the IM operational condition is quasi-steady-state.
    The experimental validation results show that the practical implementation of model-based diagnosis demonstrates better sensitivity and performance than MCSA.
    The combination of model-based diagnosis and machine learning classifier demonstrates better accuracy in fault diagnosis decisions.

    Recommendation Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i Qualification Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi Nomenclatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Statements . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Objectives of the Study . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.5 Dissertation Outlines . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.6 Dissertation related Publications . . . . . . . . . . . . . . . . . . . . 9 2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1 Motor Current Signature Analysis . . . . . . . . . . . . . . . . . . . . 11 2.1.1 Misalignment/Unbalance . . . . . . . . . . . . . . . . . . . . . 13 2.1.2 Bearing Fault . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.1.3 Broken Rotor Bar . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2 Enhancement of Low Amplitude Fault Frequency Signature . . . . . 16 2.3 Model-based Diagnosis and Induction Motor Model Identification . . 18 2.4 Feature Extraction Methods in Fault Diagnosis based on Machine Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.5 Research Gap Analysis on the Proposed Approach . . . . . . . . . . . 23 3 Data-Driven Modeling of Induction Motors . . . . . . . . . . . . . . . . . . 26 3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2 Coordinate Transformation . . . . . . . . . . . . . . . . . . . . . . . 27 3.3 Induction Motor Dynamic Model . . . . . . . . . . . . . . . . . . . . 27 3.4 Black-box State-Space Realization . . . . . . . . . . . . . . . . . . . . 29 3.5 Subspace Identification Algorithm . . . . . . . . . . . . . . . . . . . . 30 3.5.1 State-Space Model . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5.2 Data Representation . . . . . . . . . . . . . . . . . . . . . . . 31 3.5.3 State Sequences and Extended Observability Matrix . . . . . 32 3.5.4 System Matrices Estimation . . . . . . . . . . . . . . . . . . . 34 3.5.5 Stochastic Quantities Estimation . . . . . . . . . . . . . . . . 35 3.5.6 Initial States Estimation . . . . . . . . . . . . . . . . . . . . . 35 3.5.7 Persistently Exciting Input Signals . . . . . . . . . . . . . . . 36 3.6 V/I Signals Pair Correction . . . . . . . . . . . . . . . . . . . . . . . 37 3.7 Current Signals Estimation . . . . . . . . . . . . . . . . . . . . . . . 39 3.8 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.9 Model Identification Results . . . . . . . . . . . . . . . . . . . . . . . 42 3.9.1 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.9.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4 Model-based Diagnosis Framework . . . . . . . . . . . . . . . . . . . . . . 49 4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.3 SID-based Residual Generator . . . . . . . . . . . . . . . . . . . . . . 52 4.4 Signal Processing and Frequency Analysis . . . . . . . . . . . . . . . 54 4.4.1 Fast-Fourier Transform . . . . . . . . . . . . . . . . . . . . . . 54 4.4.2 Fault-related Frequency . . . . . . . . . . . . . . . . . . . . . 56 4.4.3 Rotational Frequency and Slip Estimations . . . . . . . . . . 57 4.4.4 Local-band Search . . . . . . . . . . . . . . . . . . . . . . . . 59 4.4.5 Rotational Frequency Tracking in Current Spectrum . . . . . 59 4.4.6 Picket Fence Effect . . . . . . . . . . . . . . . . . . . . . . . . 60 4.5 Fault Diagnosis Framework . . . . . . . . . . . . . . . . . . . . . . . 61 4.5.1 Statistical Residual Threshold . . . . . . . . . . . . . . . . . . 61 4.5.2 Residual Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . 62 4.6 Fault Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.7 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.7.1 Single-Fault Diagnosis: Broken Rotor Bar . . . . . . . . . . . 67 4.7.2 Single-Fault Diagnosis: Bearing Fault . . . . . . . . . . . . . . 71 4.7.3 Multiple-Fault Diagnosis . . . . . . . . . . . . . . . . . . . . . 74 4.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5 Integrated Model-based and Machine Learning Diagnosis Framework . . . 80 5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.2 Signal-based v.s. Model-based . . . . . . . . . . . . . . . . . . . . . . 81 5.3 Residual Sensitivity to Faults . . . . . . . . . . . . . . . . . . . . . . 83 5.4 Fault Isolability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.5 Integrated Diagnosis Framework . . . . . . . . . . . . . . . . . . . . . 87 5.6 Machine Learning Classifiers . . . . . . . . . . . . . . . . . . . . . . . 88 5.6.1 Support Vector Machines . . . . . . . . . . . . . . . . . . . . 89 5.6.2 Artificial Neural Network . . . . . . . . . . . . . . . . . . . . 90 5.7 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.7.1 Experimented Faults . . . . . . . . . . . . . . . . . . . . . . . 92 5.7.2 Dataset Preparation . . . . . . . . . . . . . . . . . . . . . . . 93 5.8 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5.8.1 Feature Distributions . . . . . . . . . . . . . . . . . . . . . . . 94 5.8.2 Fault Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 6 Conclusions and Future Works . . . . . . . . . . . . . . . . . . . . . . . . . 101 6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 6.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 APPENDIX A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 A.1 Three-phase Voltage Equations . . . . . . . . . . . . . . . . . . . . . 116 A.2 Arbitrary αβ0 Reference Frame . . . . . . . . . . . . . . . . . . . . . 118 A.2.1 Voltage Equations in Arbitrary Frame . . . . . . . . . . . . . 119 A.2.2 Flux Linkage in Arbitrary Frame . . . . . . . . . . . . . . . . 120 A.2.3 Rotor Torque in Arbitrary Frame . . . . . . . . . . . . . . . . 121 A.3 Induction Motor Model in Stationary Frame . . . . . . . . . . . . . . 123 APPENDIX B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 B.1 Hankel Matrix and QR Decomposition . . . . . . . . . . . . . . . . . 124 B.2 I/O Data Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 B.3 Projection of Zi Calculation . . . . . . . . . . . . . . . . . . . . . . . 125 B.4 Projection of Zi+1 Calculation . . . . . . . . . . . . . . . . . . . . . . 126 B.5 Oblique Projection of Oi Calculation . . . . . . . . . . . . . . . . . . 126 B.6 State-Space Matrices Calculation . . . . . . . . . . . . . . . . . . . . 127 APPENDIX C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 C.1 Magnelab MGC-1000-050 . . . . . . . . . . . . . . . . . . . . . . . . 129 APPENDIX D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 D.1 Single Fault Diagnosis: Broken Rotor Bar . . . . . . . . . . . . . . . 130 D.2 Single Fault Diagnosis: Bearing Fault . . . . . . . . . . . . . . . . . . 131 D.3 Multiple Fault Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . 132

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