An efficient diagnosis approach for bearing faults using sound quality metrics

Mian, Tauheed; Choudhary, Anurag; Fatima, Shahab

doi:10.1016/j.apacoust.2022.108839

Cited by 34 publications

(6 citation statements)

References 37 publications

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“…ASD task involve detecting and analyzing the sounds emitted by target objects to determine if their operational status is abnormal. As abnormal sounds may indicate equipment in a faulty state, timely detection of abnormal operational states can prevent more severe issues [6,7]. Deep learning-based anomaly sound detection systems are applied to industrial equipment inspection tasks, providing rapid and accurate analysis of equipment operational status, thereby achieving automated detection and predictive maintenance of machinery [8,9].…”

Section: Introductionmentioning

confidence: 99%

Anomaly sound detection of industrial devices by using teacher-student incremental continual learning

Zhou,

Wang,

et al. 2024

Meas. Sci. Technol.

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Normal production processes will be substantially impacted by industrial devices in abnormal working conditions. Anomaly sound detection (ASD) model can monitor the working condition of devices by the non-contact and non-invasive way. When new device data is introduced, traditional ASD models are trained using data from all devices, to accommodate every device. However, in real-world settings, the kinds and amounts of devices are constantly changing, which raises difficulties for the current ASD models. This paper proposes a teacher-student incremental learning method for ASD models, aiming to solve ASD model scalability problem. In this paradigm, teacher model has knowledge of all the old devices. The objective of student model is to learn new device knowledge, while avoiding the forgetting of old device knowledge. When student model learns new device data, teacher model transfers the acoustic feature knowledge of old devices to student model via knowledge distillation. Furthermore, the imbalance between old and new knowledge causes challenges, such as knowledge forgetting or lower learning efficiency for student model. This paper presents a dual-teacher-student (DTS) model to solve the problem of knowledge imbalance. Different teacher models for new and old devices in DTS, directing student model to accomplish continuous and deep integration of knowledge. Evaluation for proposed method on the DCASE 2020 Task2 dataset. The results show, the proposed method outperforms other methods in terms of learning capability and robustness during the incremental learning process. Analysis of significance test on the experimental results demonstrates that the method outperforms other methods statistically.

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Section: Introductionmentioning

confidence: 99%

Anomaly sound detection of industrial devices by using teacher-student incremental continual learning

Zhou,

Wang,

et al. 2024

Meas. Sci. Technol.

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“…The failure of anti-friction bearings may cause abnormal operation of mechanical equipments, and even cause serious economic losses and casualties 1 – 4 . Therefore, in order to ensure the normal operation of rotating equipment, it is very important to quickly and efficiently diagnose the faults of anti-friction bearings 5 , 6 . Machine learning-based fault diagnosis model is relatively widespread, such as, the deep variational autoencoder 7 , 8 , multiscale deep belief network 9 , hybrid deep learning 10 and the stacked denoising autoencoder, and so on.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of anti-friction bearings based on Bi-dimensional ensemble local mean decomposition and optimized dynamic least square support vector machine

Xiong,

Han,

Zhang

2023

Sci Rep

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In order to ensure the normal operation of rotating equipment, it is very important to quickly and efficiently diagnose the faults of anti-friction bearings. Hereto, fault diagnosis of anti-friction bearings based on Bi-dimensional ensemble local mean decomposition and optimized dynamic least square support vector machine (LSSVM) is presented in this paper. Bi-dimensional ensemble local mean decomposition, an extension of ensemble local mean decomposition from one-dimensional signal processing to Bi-dimensional signal processing, is used to extract the features of anti-friction bearings. Moreover, an optimized dynamic LSSVM is used to fault diagnosis of anti-friction bearings. The experimental results show that Bi-dimensional ensemble local mean decomposition is superior to Bi-dimensional local mean decomposition, optimized dynamic LSSVM is superior to traditional LSSVM, and the proposed Bi-dimensional ensemble local mean decomposition and optimized dynamic LSSVM method is effective for fault diagnosis of anti-friction bearings.

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“…But it has some constraints, including installing accelerometers at appropriate positions, signal amplification using an amplifier, and frequent calibration (Chen & Zhao, 2022). These issues can be overcome by non-contact sensors like acoustic sensors (Mian et al, 2022a) and IRT (Choudhary et al, 2020). The acoustic sensor can measure the generated sound by the motor, and the infrared thermal camera can measure the surface temperature of the motor in a non-contact manner.…”

Section: Introductionmentioning

confidence: 99%

Nature‐inspired artificial bee colony‐based hyperparameter optimization of CNN for anomaly detection in induction motor

et al. 2023

Self Cite

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The Induction Motor (IM) is one of the most frequently used prime movers in most industrial and transportation systems. The motor's stable and safe operation directly influences the secure and reliable operation of such prime movers. Developing an intelligent fault diagnosis system for such motors is very significant. This paper presents an intelligent fault diagnosis method based on the improved functionality of a Convolutional Neural Network (CNN) through its hyperparameter optimization using a nature‐inspired Artificial Bee Colony Optimization (ABCO) algorithm. The proposed diagnostic method introduces and analyses various possible mechanical and electrical faults in the IM. The validation of the proposed method is presented with three different modalities, including vibration, acoustic, and infrared thermography, with their comparative performance analysis. Vibration and acoustic‐based detection are done with time‐frequency scalograms using Constant Q Transform (CQT), which provides enhanced time resolution for lower and higher frequencies. The obtained result indicates that the infrared thermography‐based anomaly detection outperforms the vibration and acoustic‐based diagnosis with 100% classification accuracy. The results signify the potential to diagnose different mechanical and electrical faults in IM with substantial reliability and robustness.

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An efficient diagnosis approach for bearing faults using sound quality metrics

Cited by 34 publications

References 37 publications

Anomaly sound detection of industrial devices by using teacher-student incremental continual learning

Anomaly sound detection of industrial devices by using teacher-student incremental continual learning

Fault diagnosis of anti-friction bearings based on Bi-dimensional ensemble local mean decomposition and optimized dynamic least square support vector machine

Nature‐inspired artificial bee colony‐based hyperparameter optimization of CNN for anomaly detection in induction motor

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