DCNN-Based Multi-Signal Induction Motor Fault Diagnosis

Shao, Siyu; Yan, Ruqiang; Lu, Yao; Wang, Peng; Gao, Robert X.

doi:10.1109/tim.2019.2925247

Cited by 261 publications

(77 citation statements)

References 48 publications

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“…The cross-entropy function [120] is expected to perform better at improving the efficacy of training models, compared with traditional square error objective functions. Related works where cross-entropy is used for the construction of deep learning models refer to [121]- [125]. Because ShanEn is always smaller or equal than crossentropy (Sec.…”

Section: Entropy Usage In Pattern Recognitionmentioning

confidence: 99%

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Huo

Martínez-García

Zhang

et al. 2020

IEEE Trans. Instrum. Meas.

Self Cite

112

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Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent example is the design of machine condition monitoring and industrial fault diagnostic systems. The occurrence of failures in a machine will typically lead to non-linear characteristics in the measurements, caused by instantaneous variations, which can increase the complexity in the system response. Entropy measures are suitable to quantify such dynamic changes in the underlying process, distinguishing between different system conditions. However, notions of entropy are defined differently in various contexts (e.g., information theory and dynamical systems theory), which may confound researchers in the applied sciences. In this paper, we have systematically reviewed the theoretical development of some fundamental entropy measures and clarified the relations among them. Then, typical entropy-based applications of machine fault diagnostic systems are summarized. Further, insights into possible applications of the entropy measures are explained, as to where and how these measures can be useful towards future datadriven fault diagnosis methodologies. Finally, potential research trends in this area are discussed, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault diagnostic systems.

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Section: Entropy Usage In Pattern Recognitionmentioning

confidence: 99%

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Huo

Martínez-García

Zhang

et al. 2020

IEEE Trans. Instrum. Meas.

Self Cite

112

View full text Add to dashboard Cite

show abstract

“…In many cases, due to limited data availability and the characteristics of input signals, it is not feasible to apply deep networks and models frequently settle at local optima [19]. Inspired by demonstrated success of CNN in many image-related tasks, some recent efforts sought to operate CNN on vibration signals and FFT responses visualized as images [29]. However, this formulation might prevent the CNN to properly extract desired features from the input.…”

Section: A 1-d Cnnmentioning

confidence: 99%

Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery

Chen

Meng

Tang³

et al. 2020

IEEE/ASME Trans. Mechatron.

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Fault diagnosis for rolling elements in rotating machinery persistently receives high research interest due to the said machinery's prevalence in a broad range of applications. State-of-the-art methods in such setups focus on effective identification of faults that usually involve a single component while rejecting noise from limited sources. This article studies the data-based diagnosis of mixed faults coming from multiple components with an emphasis on model robustness against a wide spectrum of external perturbation. A dataset is collected on a rotor and bearing system by varying the levels and types of faults in both the rotor and bearing, which results in 48 machine health conditions. A duplet classifier is developed by combining two 1-D convolutional neural networks (CNNs) that are responsible for the diagnosis of the rotor and bearing faults, respectively. Experimental results show that the proposed classifier can reliably identify the onset and nature of mixed faults. In addition, one-vs-all classifiers are built using the features generated by the developed 1-D CNNs as predictors to recognize previously unlearned fault types. The effectiveness of such classifiers is demonstrated using data collected from four new fault types. Finally, the robustness and ability to reject external perturbation of the duplet classification model are analyzed using kernel density estimation. The code for the proposed classifiers is available at https://github.com/siyuanc2/machine-fault-diag.

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“…Over the past three years, there has been a growing interest in DNN-based electrical machine damage detection systems noticed. For the most part, they are associated with the analysis of mechanical damage to the IM [35,36] or mechanical system components [37,38]. A small number of works related to electrical damage of IM mainly concern rotor damages [24,27].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

et al. 2020

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In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.

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DCNN-Based Multi-Signal Induction Motor Fault Diagnosis

Cited by 261 publications

References 48 publications

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery

Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

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