For a rotary machine vibration signal collected under variable speed conditions, its time–
frequency representation (TFR) contains abundant oscillatory components with time-varying …

Sharp speed variation leads to a shift of sample distribution domain, which poses a
challenge for vibration-based rolling bearing fault diagnosis. Furthermore, the overfitting …

State-of-the-art deep learning methods have demonstrated impressive performance in the
intelligent fault diagnosis of rolling element bearings. However, in previous studies, critical …

Condition monitoring of rolling bearings is attracting much interest since most of the
production slowdowns depends on the damaging of these components. Several algorithms …

Rolling element bearings (REBs) are an essential part of rotating machinery. A localised
defect in a REB typically results in periodic impulses in vibration signals at bearing …

Using multi-source sensing data based on the Internet of things and fusion in conjunction
with fuzzy convolutional neural networks to classify and predict mechanical failures has …

Traditional bearing fault diagnosis is mainly based on periodic feature extraction from
bearing fault pulses. However, the diagnostic accuracy was limited due to the absence of …

With the accumulation of data, the intelligent fault diagnosis of rolling bearings has achieved
fruitful results, but it is costly to acquire and label data for industrial application. A series of …

Accurate rotational speed information is typically required to diagnose bearing failures
under variable speed conditions. This paper proposes a fault localization method to obtain …

Under the condition of linear varying speed regulation, the vibration signal of the rolling
bearing is non-stationary, and its fault frequency is time-varying, which makes it difficult to …