The acquired hob vibration signals are inevitably contaminated by noise in the industrial
environment, which changes the vibration signal frequency distribution and reduces the …

Feature extraction from vibration signals plays a vital role in rotating machinery fault
diagnosis. The noise contained in the signals will interfere with the fault feature extraction …

This work presents a leakage identification framework for the coal mine air supply pipeline
system based on joint noise reduction and extreme learning machine (ELM). First, leakage …

Accurately identifying the health status of train running gear bearings is crucial to ensure the
quality of operation. As the early fault information of bearings is weak and submerged in the …

This paper proposes a modeling and optimization method for cement clinker calcination
process based on gaussian process regression ensemble (EGPR) model and steady-state …

The traditional one-dimensional denoising method, commonly applied in vibration signal
processing, would encounter issues as the calculation complexity, difficulty in maintaining …

Current mainstream wear diagnosis approaches rely on the entire lifecycle wear-labeled
datasets, which is costly, delayed, and low-universality. To achieve the monitoring goal of …

Water scarcity is a pressing issue, intensified by factors such as population growth and
industrialization. Hence, it is crucial to monitor, conserve and analyse groundwater …

The nonlinear ultrasonic detection signals of concrete are highly sensitive to micro-damage
changes and are thus susceptible to noise interference. Consequently, denoising these …

To address the issue that the deep learning-based denoising algorithms can hardly
effectively eliminate the background noise under small sample data condition, this paper …