Intelligent Fault Diagnosis Technology for Mechanical Equipment: Current Status and Development
DOI:
https://doi.org/10.63313/SD.2002Keywords:
mechanical equipment, fault diagnosis technology, current status and developmentAbstract
With the continuous advancement of technology, mechanization is also progressing, and automated and intelligent technologies are being increasingly developed. Machinery and equipment are exerting a growing influence in modern industrial technology. However, due to their complexity, malfunctions during operation can disrupt the entire operational system. Such disruptions can lead to significant economic losses and potentially cause injuries or fatalities in mechanical engineering or construction projects. Therefore, intelligent fault diagnosis of mechanical equipment is crucial. It not only ensures the normal operation of machinery but also enhances efficiency throughout the system, thereby fostering the development of industrial production.
References
[1] Zhu J ,Hu J ,Sheng B .A label enhancement based positive-unlabeled hybrid network for pump bearing intelligent fault diagnosis[J].Applied Soft Compu-ting,2025,185(PB):113976-113976.
[2] Li X ,Yao Y ,Li L , et al.Intelligent vortex optimization method for multi-objective VMD in mechanical fault diagnosis[J].International Journal of Mechanical Scienc-es,2025,306110860-110860.
[3] Wang R ,Hu J ,Xin D , et al.Robust subspace tracking in intelligent fault diagnosis of digital twin gas turbines base on the adaptive Markov transfer[J].Applied Ener-gy,2025,401(PC):126747-126747.
[4] Tang J ,Peng S ,Guo J , et al.DT and LLM driven intelligent maintenance system for L-DED and DAG-based LLM fault diagnosis evaluation framework[J].Applied Soft Compu-ting,2025,185(PA):113942-113942.
[5] Robles A G ,Espitia A F ,Prieto D M , et al.Non-contact intelligent fault diagnosis based on thermography, unsupervised feature modelling and deep-feature learning for assessing faults in electromechanical systems[J].Infrared Physics and Technolo-gy,2025,150106039-106039.
[6] Lu Z ,Sun C ,Li X .Multimodal Large Language Model-Enabled Machine Intelligent Fault Di-agnosis Method with Non-Contact Dynamic Vision Data.[J].Sensors (Basel, Switzer-land),2025,25(18):5898-5898.
[7] Li J ,Wu Q .Parallel CNN small sample ball screw intelligent fault diagnosis method based on symmetric dot pattern and wavelet synchronous extraction transform[J].Engineering Research Express,2025,7(3):035566-035566.
[8] Gao B ,Ge H ,Li H , et al.Research on Intelligent Fault Diagnosis System for Online Monitor-ing of Drilling Units[J].Journal of Physics: Conference Se-ries,2025,3048(1):012134-012134.
[9] Song Y ,Wang R ,Wang J , et al.GCS-EN-SCNs-wd: a generalized class-specific cost-sensitive mechanism to regulate stochastic configuration networks with direct link for transformer fault diagnosis[J].Measurement Science and Technology,2025,36(8):086137-086137.
[10] Hu Y ,Cheng S ,Du X .Intelligent Fault Diagnosis for Rotating Machinery Utilizing Gramian Angular Field-Parallel Convolutional Neural Network and Gated Recurrent Unit Net-works[J].Applied Sciences,2025,15(16):9217-9217.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 by author(s) and Erytis Publishing Limited.

This work is licensed under a Creative Commons Attribution 4.0 International License.













