In the field of predictive maintenance (PdM), machine learning (ML) has gained importance
over the last years. Accompanying this development, an increasing number of papers use …

This paper presents an extensive literature review on the field of condition-based
maintenance (CBM). The paper encompasses over 4000 contributions, analysed through …

Electric vehicle (EV) is crucial for future transportation which will improve fuel economy and
contributes toward the reduction of emissions. EVs are becoming an increasingly integrated …

Despite the complex mathematical models and physical phenomena on which it is based,
the simplicity of its construction, its affordability, the versatility of its applications and the …

This paper applies the Logical Analysis of Data (LAD) to detect and diagnose faults in
industrial chemical processes. This machine learning classification technique discovers …

Timely failure detection for bearings is of great importance to prevent economic losses in the
industry. In this article we propose a method based on Convolutional Neural Networks …

Due to its importance in the industry, vibration-based diagnosis and prognosis of rolling
element bearings (REB) attract more and more attention from the research community …

Advance prediction about bearing remaining useful life (RUL) is a major activity which aims
at scheduling proper future actions to avoid catastrophic events. However, the reliability of …

This paper presents a novel methodology for multiple failure modes prognostics in rotating
machinery. The methodology merges a machine learning and pattern recognition approach …

Most of the reported prognostic techniques use a small number of condition indicators
and/or use a thresholding strategies in order to predict the remaining useful life (RUL). In this …