Multi-fault diagnosis of Industrial Rotating Machines using Data-driven approach : A review of two decades of research

Gawde, Shreyas; Patil, Shruti; Kumar, Satish; Kamat, Pooja; Kotecha, Ketan; Abraham, Ajith

doi:10.1016/j.engappai.2023.106139

Cited by 70 publications

(11 citation statements)

References 95 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the rapid development of society and technology, largescale mechanical equipment is becoming increasingly automated and intelligent. As a common component of large rotating machinery such as wind turbines, aviation engines, ships, and high-speed trains, bearings' condition monitoring and fault diagnosis are of great significance for ensuring the safety of mechanical equipment [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A new model for bearing fault diagnosis based on mutual mapping of signals and images and sparse representation

Yang,

Bai,

Tan

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

For the issue of significant noise in the collected bearing fault signals, a new bearing fault diagnosis model based on mutual mapping of signals and images (MMSI) and sparse representation (SR) denoising is proposed. Firstly, the fault signal is divided into several segments with the same number of sampling points, and then arrange these segments in ascending order of rows. Secondly, convert the arranged signals into grayscale image and use dictionary learning for block denoising. Then, the de-noised grayscale image is restored to a signal in line order. Finally, k-nearest neighbor (KNN) is used for fault classification. To verify the performance of the proposed model, experiments are tested on 12 single working conditions and 30 multi working conditions on the Case Western Reserve University (CWRU) dataset and the Paderborn dataset. The experimental results indicate that compared with some existing models, the MMSI-SR-KNN model can not only accurately diagnose bearing faults in artificial damage experiments, but also performs better in real damage faults. This indicates that the model has good generalization ability between different datasets and working conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

A new model for bearing fault diagnosis based on mutual mapping of signals and images and sparse representation

Yang,

Bai,

Tan

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Researchers have been working to create a generalised method for fault diagnosis in rotating machines for the past few years [3], focussing mainly on a) fault pattern identification and b) developing a classification algorithm to distinguish the faults based on the patterns. Recently, predictive maintenance techniques have been getting important where multiple sensors' data is used to predict the machinery condition using various AI algorithms [4]. Online condition monitoring is another rising technique that allows online access to the health data of these machines [5].…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers have also used the hybrid of ML and DL algorithms for better results [15]. A systematic literature review on multi-fault diagnosis in rotating machines is addressed by authors in [4] and [46].…”

Section: Introductionmentioning

confidence: 99%

Explainable Predictive Maintenance of Rotating Machines Using LIME, SHAP, PDP, ICE

Gawde,

Patil,

Kumar

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

Artificial Intelligence (AI) is a key component in Industry 4.0. Rotating machines are critical components in manufacturing industries. In the vast world of Industry 4.0, where an IoT network acts as a monitoring and decision-making system, predictive maintenance is quickly gaining importance. Predictive maintenance is a method that uses AI to handle potential problems before they cause breakdowns in operations, processes or systems. However, there is a significant issue with the AI models' (also known as "black boxes") inability to explain their decisions. This interpretability is vital for making maintenance decisions and validating the model's reliability, leading to improved trust and acceptance of AI-driven predictive maintenance strategies. Explainable AI is the solution because it provides human-understandable insights into how the AI model arrives at its predictions. In this regard, the paper presents Explainable AIbased predictive maintenance of Industrial rotating machines. The proposed approach unfolds in four comprehensive stages: a) Multi-sensor based multi-fault (5 different fault classes) data acquisition, b) Frequency-domain statistical feature extraction, c) Comparison of results for multiple AI algorithms, and d) XAI integration using "Local Interpretable Model Agnostic Explanation (LIME)", "SHapley Additive exPlanation (SHAP)", "Partial Dependence Plot (PDP)" and "Individual Conditional Expectation (ICE)" to interpret the results.

show abstract

“…However, the research on intermittent fault diagnosis of circuit system is still very few. Taking the intermittent fault and conventional fault related to electrical system as the object, this paper comprehensively introduces two types of mainstream diagnosis methods, namely, the methods based on signal processing and the methods based on deep learning [14]. For instance, a built-in test system intermittent fault identification method based on empirical mode decomposition (EMD) and hidden Markov model (HMM) is proposed [15].…”

Section: Introductionmentioning

confidence: 99%

An efficient diagnostic strategy for intermittent faults in electronic circuit systems by enhancing and locating local features of faults

Jia,

Wang,

Zhao

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Due to their short duration, concealability, and random occurrence, intermittent faults have become the most dangerous hazard in electronic circuit systems. However, existing intelligent diagnostic methods often struggle to provide substantial effectiveness for such objects. This article proposes a scheme to enhance the output signal of electronic circuits to make the features more intuitive and facilitate fault feature mining, and designs a scheme to mine tiny fault features from global signals. First, the circuit’s output signal undergoes an S-transform to obtain its time-frequency domain characteristics. Next, a sequence and excitation networks attention module is employed to allocate weights to different channels. Finally, the aforementioned output is used as input to the Swin transformer framework to thoroughly explore fault features. Three electronic circuits are used as experimental circuits to test the proposed method. The experiment shows that the proposed diagnostic method is fast and has an accuracy of over 97%. Therefore, the effectiveness of the designed strategy that includes multiple attention mechanisms in mining intermittent fault features in electronic circuit systems has been demonstrated.

show abstract

Multi-fault diagnosis of Industrial Rotating Machines using Data-driven approach : A review of two decades of research

Cited by 70 publications

References 95 publications

A new model for bearing fault diagnosis based on mutual mapping of signals and images and sparse representation

A new model for bearing fault diagnosis based on mutual mapping of signals and images and sparse representation

Explainable Predictive Maintenance of Rotating Machines Using LIME, SHAP, PDP, ICE

An efficient diagnostic strategy for intermittent faults in electronic circuit systems by enhancing and locating local features of faults

Contact Info

Product

Resources

About