Predictive maintenance (PdM) uses statistical and machine learning methods to detect and
predict the onset of faults. PdM is often used in industrial IoT settings in the energy sector …

Manufacturing downtime due to faults is costly and disruptive. With the increasing availability
of real-time data in modern Smart Manufacturing (SM) environments, effective anomaly …

Considering that large mechanical equipment often has various excitation sources, the
signals generated by these excitation sources are often not simply added or multiplied …

Complex and changeable working conditions are important factors affecting the accuracy
and robustness of diesel engine fault diagnosis models. Working condition identification can …

Fault samples of marine engine are extremely scarce, and there are unavoidably some hard
samples with small inter-class differences, which pose a serious challenge to fault diagnosis …

Predictive maintenance has been employed to reduce maintenance costs and production
losses and to prevent any failure before it occurs. The framework proposed in this work …

Diesel Engine plays a vital role in the working of various machinery, and the disruption in
working of such machines due to some faults leads to a large number of losses. The time …

Detecting and isolating faults on heavy duty vehicles is very important because it helps
maintain high vehicle performance, low emissions, fuel economy, high vehicle safety and …

This work aims to create a deep learning model utilizing Long Short-Term Memory (LSTM)
as a classification model to detect and diagnose potential problems in diesel engines. The …

For diesel engine malfunction detection, machine learning-based intelligent detection
approaches have made great strides, but some performance deterioration is also observed …