Robust Interpretable Deep Learning for Intelligent Fault Diagnosis of Induction Motors

Abid, Firas Ben; Sallem, Marwen; Braham, Ahmed

doi:10.1109/tim.2019.2932162

Cited by 92 publications

(38 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the feature mapping obtained from the first convolutional layer, KecNet has some advantages over the standard CNN in terms of interpretability and readability. This is mainly because the Sinc-convolutional structure used in the model is functionally equivalent to a bandpass filter, which learns parameters with a clear physical meaning, i.e., the high and low cut-off frequencies of the ECG signal [ 24 ]. Figure 5 shows examples of filters learned by KecNet ( Figure 5(a) ) and standard CNN ( Figure 5(b) ) using the MIT-BIH arrhythmia dataset.…”

Section: Resultsmentioning

confidence: 99%

“…The experimental results in Section 5.3 show that the Sinc-convolution layer is more selective in frequency response compared to the CNN. Because the filter effectively extracts components from complex signals over a specific frequency range, it improves the model's robustness and readability [ 24 ]. After the Sinc filter self-adaptively classifies the frequency band of the raw ECG data, the standard CNN structure is used to extract the time domain features.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement

Gao

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Acquiring electrocardiographic (ECG) signals and performing arrhythmia classification in mobile device scenarios have the advantages of short response time, almost no network bandwidth consumption, and human resource savings. In recent years, deep neural networks have become a popular method to efficiently and accurately simulate nonlinear patterns of ECG data in a data-driven manner but require more resources. Therefore, it is crucial to design deep learning (DL) algorithms that are more suitable for resource-constrained mobile devices. In this paper, KecNet, a lightweight neural network construction scheme based on domain knowledge, is proposed to model ECG data by effectively leveraging signal analysis and medical knowledge. To evaluate the performance of KecNet, we use the Association for the Advancement of Medical Instrumentation (AAMI) protocol and the MIT-BIH arrhythmia database to classify five arrhythmia categories. The result shows that the ACC, SEN, and PRE achieve 99.31%, 99.45%, and 98.78%, respectively. In addition, it also possesses high robustness to noisy environments, low memory usage, and physical interpretability advantages. Benefiting from these advantages, KecNet can be applied in practice, especially wearable and lightweight mobile devices for arrhythmia classification.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement

Gao

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Finding an intelligent CM and FD method for the rotor and IM's stator is considered a challenging task [76][77][78]. This section summarizes the challenges and future trends facing CM and FD of IM's stator and rotor.…”

Section: Challenges and Future Trendsmentioning

confidence: 99%

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

AlShorman

Alshorman

2021

IJECE

View full text Add to dashboard Cite

Nowadays, induction motor (IM) is extensively used in industry, including mechanical and electrical applications. However, three main types of IM faults have been discussed in the literature, bearing, stator, and rotor. Importantly, stator and rotor faults represent approximately 50%. Traditional condition monitoring (CM) and fault diagnosis (FD) methods require a high processing cost and much experience knowledge. To tackle this challenge, artificial intelligent (AI) based CM and FD techniques are extensively developed. However, there have been many review research papers for intelligent CM and FD machine learning methods of rolling elements bearings of IM in the literature. Whereas there is a lack in the literature, and there are not many review papers for both stator and rotor intelligent CM and FD. Thus, the proposed study's main contribution is in reviewing the CM and FD of IM, especially for the stator and the rotor, based on AI methods. The paper also provides discussions on the main challenges and possible future works.

show abstract

“…The resulting feature space is then classified [18,20] or clustered [19,21] in order to detect and classify faults and their severity, but also to detect novel fault types [19]. Robust feature learning is the objective of many publications of fault diagnosis [22,23]. These works typically focus on robustness with respect to noisy environments.…”

Section: Related Workmentioning

confidence: 99%

Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types

Rombach

Michau

Fink

2021

Sensors

View full text Add to dashboard Cite

Reliable fault detection and diagnostics are crucial in order to ensure efficient operations in industrial assets. Data-driven solutions have shown great potential in various fields but pose many challenges in Prognostics and Health Management (PHM) applications: Changing external in-service factors and operating conditions cause variations in the condition monitoring (CM) data resulting in false alarms. Furthermore, novel types of faults can also cause variations in CM data. Since faults occur rarely in complex safety critical systems, a training dataset typically does not cover all possible fault types. To enable the detection of novel fault types, the models need to be sensitive to novel variations. Simultaneously, to decrease the false alarm rate, invariance to variations in CM data caused by changing operating conditions is required. We propose contrastive learning for the task of fault detection and diagnostics in the context of changing operating conditions and novel fault types. In particular, we evaluate how a feature representation trained by the triplet loss is suited to fault detection and diagnostics under the aforementioned conditions. We showcase that classification and clustering based on the learned feature representations are (1) invariant to changing operating conditions while also being (2) suited to the detection of novel fault types. Our evaluation is conducted on the bearing benchmark dataset provided by the Case Western Reserve University (CWRU).

show abstract

Robust Interpretable Deep Learning for Intelligent Fault Diagnosis of Induction Motors

Cited by 92 publications

References 37 publications

KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement

KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types

Contact Info

Product

Resources

About