Remaining useful life estimation of ball-bearings based on motor current signature analysis

Bermeo-Ayerbe, Miguel Angel; Cocquempot, Vincent; Ocampo‐Martínez, Carlos; Díaz-Rozo, Javier

doi:10.1016/j.ress.2023.109209

Cited by 10 publications

(3 citation statements)

References 30 publications

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“…Figure 1 shows the research methodology used in this paper. First, an electric current sensor was developed to monitor the RPM equipment, which is the most applicable sensor in RPM monitoring implementations [11] and delivers a high level of precision in describing the electro-mechanical conditions [13]. Second, various measurement information was acquired for each type of RPM equipment condition using current-data acquisition (DAQ).…”

Section: Methodsmentioning

confidence: 99%

“…The implementation of a current sensor is the most practical since it does not interfere with operations and can be installed in the power supply, which is located inside the equipment room in the station area, rather than inside the RPM equipment or field area [11]. Furthermore, electric current data can be used to diagnose not just electrical but also mechanical failures [13].…”

Section: Introductionmentioning

confidence: 99%

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Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning

Sugiana,

Cahyadi,

Yusran

2023

Applied Sciences

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The majority of railway operators still implement conventional maintenance for railway point machines (RPMs), which is one of the most vital pieces of equipment for ensuring the safety of train operation. The conventional maintenance method lacks accuracy, is less efficient, and has high labor costs. This study developed a cost-effective and accurate fault diagnosis (FD) method based on current data to increase the overall efficiency of RPM maintenance. The FD method for RPM equipment discussed in this paper consists of three working conditions: normal, working, and failure. The method was proposed based on time-series current signals, which were gathered when the RPM was in operation. Time-series data were extracted and filtered using time-domain feature extraction based on scalable hypothesis testing. The selected features became the datasets for machine learning modeling. Six machine learning algorithms were compared in order to find the algorithm with the best FD accuracy. The results showed 100% accuracy for the Decision Tree and Random Forest algorithms in the FD method. The results of the FD method could be important for maintenance teams in determining suitable maintenance activities based on RPM working conditions.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning

Sugiana,

Cahyadi,

Yusran

2023

Applied Sciences

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“…Bermeo-Ayerbe et al monitored the characteristic frequencies of torque oscillations transmitted through three-phase stator currents, a health indicator of electrical signals. Building upon this, they established a multivariate exponential regression model entirely reliant on electrical signals to estimate the RUL of electromechanical systems [20]. Zhang et al addressed the issue of the Transformer model's construction being heavily dependent on an extensive number of parameters, where excessive model redundancy adversely affected predictive performance.…”

Section: Introductionmentioning

confidence: 99%

Similarity indicator and CG-CGAN prediction model for remaining useful life of rolling bearings

Yang,

Binbin,

Cancan

et al. 2024

Meas. Sci. Technol.

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To tackle the challenges of performing early fault warning and improving the prediction accuracy for the remaining useful life (RUL) of rolling bearings, this paper proposes a similarity health indicator and a predictive model of CG-CGAN, which relies on a Conditional Generative Adversarial Network (CGAN) that combines one-dimensional Convolutional Neural Network (CNN) with a Bidirectional Gate Recurrent Unit (Bi-GRU). This framework provides a comprehensive theoretical foundation for RUL prediction of rolling bearings. The similarity health indicator allows for early fault warning of rolling bearings without expert knowledge. Within the CGAN framework, the inclusion of constraints guides the generation of samples in a more targeted manner. Additionally, the proposed CG-CGAN model incorporates Bi-GRU to consider both forward and backward information, thus improving the precision of RUL forecasting. Firstly, the similarity indicator between the vibration signals of the rolling bearing over its full life span and the standard vibration signals (healthy status) is calculated. This indicator helps to determine the early deterioration points of the rolling bearings. Secondly, the feature matrix composed of traditional health indicators and similarity health indicator, is utilized to train and test the proposed CG-CGAN model for RUL prediction. Finally, to corroborate the efficacy of the proposed method, two sets of real experiment data of rolling bearing accelerated life from the Intelligent Maintenance Systems (IMS) are utilized. Experimental findings substantiate that the proposed similarity health indicator offers precocious fault alerts and precisely delineates the performance diminution of the rolling bearing. Furthermore, the put-forward CG-CGAN model achieves high-precision RUL prediction of rolling bearing.

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Robust prediction of remaining useful lifetime of bearings using deep learning

Magadán,

Granda,

Suárez

2024

Engineering Applications of Artificial Intelligence

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Remaining useful life estimation of ball-bearings based on motor current signature analysis

Cited by 10 publications

References 30 publications

Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning

Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning

Similarity indicator and CG-CGAN prediction model for remaining useful life of rolling bearings

Robust prediction of remaining useful lifetime of bearings using deep learning

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