Abstract
Gear pump failures in industrial settings are common due to their exposure to uneven high-pressure outputs within short time periods of machine operation and uncertainty. Improving the field and line clam are considered as the solutions for these failures, yet they are quite insufficient for optimal reliability. This research, therefore, suggests a method for early wear detection in gear pumps following an extensive failure modes, effects, and criticality analysis (FMECA) of an AP3.5/100 external gear pump manufactured by BESCO. To replicate this condition, fine particles of iron oxide (Fe2O3) were mixed with the experimental fluid, and the resulting vibration data were collected, processed, and exploited for wear detection. The intelligent wear detection process was explored using various machine learning algorithms following a mel-frequency cepstral coefficient (MFCC)-based discriminative feature extraction process. Among these algorithms, extensive performance evaluation reveals that the random forest classifier returned the highest test accuracy of 95.17%, while the k-nearest neighbour was the most cost efficient following cross validations. This study is expected to contribute to improved evaluations of gear pump failure diagnosis and prognostics.
Highlights
An mel-frequency cepstral coefficient (MFCC)-based early wear detection model is proposed with validations from a case study on an AP3.5/100 external gear pump manufactured by BESCO
Since even with discriminative features available for use, paramaterization plays a significant role in their efficiencies [24]. These factors have motivated this study in which our objective is to explore the efficiencies of the machine learning (ML) algorithms for vibration-based wear detection following a MFCC-based feature extraction
Development of fault detection and isolation modules have become a major interest for researchers, academic institutions, and industries in view of more accurate prognostics and health management
Summary
Industrial demands for improved productivity and reduced costs/downtime are constantly increasing, and this has further motivated the need for accurate predictive maintenance schemes against the more expensive routine-based maintenance procedures. These increasing demands are favourably being compensated with diverse state-of-the-art predictive maintenance methodologies with artificial intelligence (AI) at their core. Gear pumps are quite popular for industrial purposes due to the high cost efficiency and high performance they are associated with.
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