Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

Khemani, Varun; Azarian, Michael H.; Pecht, Michael

doi:10.3390/electronics11030451

Cited by 12 publications

(5 citation statements)

References 45 publications

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“…From which SVM came out best for our data when evaluated against the metrics in section IX, equations 65 & 66, which were used to assess the performance. This coincides with results in literature for other papers on fault classification utilizing WSN derived feature vectors [81], [83], [84].…”

Section: Classificationsupporting

confidence: 91%

“…The application of WSN to health monitoring has been explored in [81], using learnable wavelet scattering networks, for the fault diagnosis of circuits and rotating machinery, using a genetic algorithm-based optimization of second-generation wavelet transform operators. Reference [82] describes an algorithm for bearing fault detection using WSNs as a pre-processing step for feature space generation and [83] investigates the efficacy and applicability of the WSN feature domain relative to fault detection and diagnosis for the mechanical components of industrial robots.…”

Section: B Wavelet Scattering Classificationmentioning

confidence: 99%

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A Component Level Digital Twin Model for Power Converter Health Monitoring

Wileman,

Aslam,

Perinpanayagam

2023

IEEE Access

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The proliferation of Power Electronic Converters (PECs) has had a pervasive affect in a variety of industries including the power generation, automotive and aerospace sectors, where their use brings reliability to the forefront, especially in applications where safety critical and harsh environments are experienced. Continuous improvements in the power density and efficiency through extensive research into new semiconductor technologies, passive components, circuit topologies and control methodologies has seen the performance of PECs improve indubitably. However, the manifestation of stresses occurring from significant heating due to high currents and switching frequencies; which over time can cause degradation in the performance of PEC components is still a real concern. This paper outlines a methodology for monitoring the degradation of PECs over the operational lifetime by utilizing a component level, physics based Digital Twin (DT). As well as providing a methodology for the real time comparison of parameters to realize the onset of faults subjected to operational stresses, the DT also provides a novel method of training a classifier by simulating the faults within the PEC, a process that in reality, would be difficult to achieve from a physical device. Feature extraction is via Wavelet Scattering and classification is provided using a Support Vector Machine (SVM) approach. The overall approach is one that is novel and expandable to a wide range of PEC topologies and will be beneficial to the optimization and maintenance of PECs in a variety of platforms.INDEX TERMS Digital twin, power electronics, reliability, power system simulation, real-time systems, health monitoring, PHM, wavelet scattering, support vector machine.

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Section: Classificationsupporting

confidence: 91%

Section: B Wavelet Scattering Classificationmentioning

confidence: 99%

A Component Level Digital Twin Model for Power Converter Health Monitoring

Wileman,

Aslam,

Perinpanayagam

2023

IEEE Access

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“…The illustration was created by Varun Khemani et al. [25] and is used, without modifications made by the author, under the Creative Commons BY license.…”

Section: Scattering Descriptormentioning

confidence: 99%

“…In this example three different wavelets are used for the convolution, which gives 3 𝑖 nodes for layer 𝑖. The illustration was created by Varun Khemani et al[25] and is used, without modifications made by the author, under the Creative Commons BY license.…”

mentioning

confidence: 99%

Scattering transform in microstructure reconstruction

Reck,

Seibert,

Raßloff

et al. 2023

Proc Appl Math and Mech

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Descriptor‐based microstructure characterization plays a crucial role in the field of reversed material engineering for random heterogeneous media. With the advent of differentiable microstructure characterization and reconstruction, there has been a growing interest in the development of differentiable formulations of descriptors. The search for effective descriptors becomes indispensable to adequately characterize a wide range of microstructures. This work proposes a novel approach to construct a descriptor by utilizing a wavelet‐based transformation called the scattering transformation on microstructure images. The characterization and reconstruction capabilities of this newly developed descriptor are compared to a benchmark descriptor based on spatial correlation functions using various 2D microstructure images. The comparative analysis aims to evaluate the effectiveness and potential advantages of the proposed wavelet‐based descriptor.

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“…Wavelet time scattering is a suitable feature extractor as well as the phase before exploring fault indicators because they maintain translation invariance, deformations, and high-frequency information [31]. Numerous studies have established the ability of WTS in feature extraction of rotating devices.…”

Section: Introductionmentioning

confidence: 99%

Classification of Unbalanced and Bowed Rotors under Uncertainty Using Wavelet Time Scattering, LSTM, and SVM

et al. 2023

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A growing interest in intelligent fault detection may sometimes lead to practical issues when existing malfunctions reveal analogous indications and the number of observations is limited. This article addresses the classification problem of two identical malfunctions, i.e., unbalancing and shaft bow in rotary machines, where only 56 observations were utilized for the training. The faulty systems are modeled in ABAQUS/CAE; a data set for each fault is created by simulation under various physical and operational conditions employing the uncertainty concept. The wavelet time scattering (WTS) technique extracts low-variance presentations from signals. With respect to the classification procedure of the faulted rotor systems, two models are examined with the extracted features from WTS as the input. Initially, a long short-term memory (LSTM) network is trained and tested, and then, the capability of a support vector machine (SVM) model is inquired. Ultimately, the classification models are trained and tested using the raw time series data and the extracted features to compare the effectiveness of the suggested methods, i.e., WTS. The employed approach for feature extraction demonstrated remarkable effectiveness in addressing a potential hurdle in identifying faults in rotating systems: the ability to differentiate between unbalanced and bowed rotors, irrespective of the classification model utilized.

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Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

Cited by 12 publications

References 45 publications

A Component Level Digital Twin Model for Power Converter Health Monitoring

A Component Level Digital Twin Model for Power Converter Health Monitoring

Scattering transform in microstructure reconstruction

Classification of Unbalanced and Bowed Rotors under Uncertainty Using Wavelet Time Scattering, LSTM, and SVM

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