Applications of machine learning to machine fault diagnosis: A review and roadmap

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Deep learning can be applied to the field of fault diagnosis for its powerful feature representation capabilities. When a certain class fault samples available are very limited, it is inevitably to be unbalanced. The fault feature extracted from unbalanced data via deep learning is inaccurate, which can lead to high misclassification rate. To solve this problem, new generator and discriminator of Generative Adversarial Network (GAN) are designed in this paper to generate more discriminant fault samples using a scheme of global optimization. The generator is designed to generate those fault feature extracted from a few fault samples via Auto Encoder (AE) instead of fault data sample. The training of the generator is guided by fault feature and fault diagnosis error instead of the statistical coincidence of traditional GAN. The discriminator is designed to filter the unqualified generated samples in the sense that qualified samples are helpful for more accurate fault diagnosis. The experimental results of rolling bearings verify the effectiveness of the proposed algorithm.

Deep Learning Fault Diagnosis Method Based on Global Optimization GAN for Unbalanced Data

Permanent magnet synchronous motor and power electronics-based three-phase inverter are the major components in the modern industrial electric drive system, such as electrical actuators in an all-electric subsea Christmas tree. Inverters are the weakest components in the drive system, and power switches are the most vulnerable components in inverters. Fault detection and diagnosis of inverters are extremely necessary for improving drive system reliability. Motivated by solving the uncertainty problem in fault diagnosis of inverters, which is caused by various reasons, such as bias and noise of sensors, this paper proposes a Bayesian network-based data-driven fault diagnosis methodology of three-phase inverters. Two output line-to-line voltages for different fault modes are measured, the signal features are extracted using fast Fourier transform, the dimensions of samples are reduced using principal component analysis, and the faults are detected and diagnosed using Bayesian networks. Simulated and experimental data are used to train the fault diagnosis model, as well as validate the proposed fault diagnosis methodology.

A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems

This article gives an overview of the artificial intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics. This article is accompanied by an Excel file listing the relevant publications for statistical analytics.

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An Overview of Artificial Intelligence Applications for Power Electronics

Multilevel inverters, for their distinctive performance, have been widely used in high voltage and high-power applications in recent years. As power electronics equipment reliability is very important and to ensure multilevel inverter systems stable operation, it is important to detect and locate faults as quickly as possible. In this context and to improve fault diagnosis accuracy and efficiency of a cascaded H-bridge multilevel inverter system (CHMLIS), a fault diagnosis strategy based on the principle component analysis and the multiclass relevance vector machine (PCA-mRVM), is elaborated and proposed in this paper. First, CHMLIS output voltage signals are selected as input fault classification characteristic signals. Then, a fast Fourier transform is used to preprocess these signals. PCA is used to extract fault signals features and to reduce samples dimensions. Finally, an mRVM model is used to classify faulty samples. Compared to traditional approaches, the proposed PCA-mRVM strategy not only achieves higher model sparsity and shorter diagnosis time, but also provides probabilistic outputs for every class membership. Experimental tests are carried out to highlight the proposed PCA-mRVM diagnosis performances.

Cascaded H-Bridge Multilevel Inverter System Fault Diagnosis Using a PCA and Multiclass Relevance Vector Machine Approach

Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method.

Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter

This paper proposes an imbalance fault detection method based on data normalization and Empirical Mode Decomposition (EMD) for variable speed direct-drive Marine Current Turbine (MCT) system. The method is based on the MCT stator current under the condition of wave and turbulence. The goal of this method is to extract blade imbalance fault feature, which is concealed by the supply frequency and the environment noise. First, a Generalized Likelihood Ratio Test (GLRT) detector is developed and the monitoring variable is selected by analyzing the relationship between the variables. Then, the selected monitoring variable is converted into a time series through data normalization, which makes the imbalance fault characteristic frequency into a constant. At the end, the monitoring variable is filtered out by EMD method to eliminate the effect of turbulence. The experiments show that the proposed method is robust against turbulence through comparing the different fault severities and the different turbulence intensities. Comparison with other methods, the experimental results indicate the feasibility and efficacy of the proposed method.

Tianzhen Wang

Papers

Cascaded H-Bridge Multilevel Inverter System Fault Diagnosis Using a PCA and Multiclass Relevance Vector Machine Approach

Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter

An imbalance fault detection method based on data normalization and EMD for marine current turbines.

An adaptive confidence limit for periodic non-steady conditions fault detection

EEMD-based notch filter for induction machine bearing faults detection