TL;DR
A method for a wear condition diagnosis and health state assessment of gearboxes using sensor data and machine learning is developed and provides industry the capability of predictive maintenance which can minimize loss of operational availability and improve gear.
The findings of this study demonstrate the potential of lubrication data to forecast machine health and improve equipment reliability, providing valuable insights that enable researchers to enhance lubrication management strategies.
In the context of the accelerating process of digitalization and automation of coal mines, the role of gearboxes in the mining industry is undeniable. The stability of their operational condition is directly related to the safety and efficiency of coal production. However, considering their widespread industrial use, g...
Rolling element bearings are used in rotating machines in aviation, chemical, and nuclear industries. A failure to detect faults in the rolling bearing causes unexpected breakdown of rotating machines. Detecting bearing defects early on is still a challenge since micro- faults have less energy. Early-stage defects fro...
In industrial production settings, mechanical equipment may suffer from wear, cracking, and fracture due to harsh conditions such as variable loads, high impacts, and strong disturbances, leading to performance degradation, production halts, and even catastrophic accidents. With the rapid advancement of deep learning,...
A standardized benchmarking of multi-fidelity methodologies and explainability-augmented models is suggested, which shows that on these benchmarks, convolutional and recurrent deep architectures, attention-augmented LSTMs, and ensemble methods consistently outperform classical classifiers.
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