Special Issue on Machine Condition Monitoring and Fault Diagnosis: From Theory to Application

Cong, Feiyun

doi:10.3390/app132011550

Cited by 1 publication

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“…In order to optimize the effectiveness of machine operation, reduce unplanned downtime, and decrease operational and maintenance expenses, it becomes imperative to create an intelligent fault diagnosis (FD) method for RPM equipment. The method should be designed to evaluate the health status, aiming to identify the type, severity, and degradation trend of potential faults [6]. The FD method also has a big impact on increasing the quality and efficiency of maintenance by providing appropriate maintenance recommendations based on the current state of the equipment [7].…”

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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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: Introductionmentioning

confidence: 99%

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

Sugiana,

Cahyadi,

Yusran

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

Special Issue on Machine Condition Monitoring and Fault Diagnosis: From Theory to Application

Abstract: Modern machines are becoming more complex in structure and are operating under harsher loading and operational conditions [...]

Cited by 1 publication

References 6 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

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