In recent years, machine learning techniques have been proven to be a promising tool for
early fault detection of rolling bearings. In many actual applications, however, bearing whole …

The growing demand for high-quality industrial products has led to a significant emphasis on
image anomaly detection (AD). AD in industrial goods presents a formidable research …

The anomaly detection for multimode industrial process is a challenging problem, because
the multiple operation modes present various main distributions of monitored variables, and …

Aiming at the problem of low accuracy of unsupervised learning anomaly detection
algorithm, a dual-input anomaly detection method based on deep reinforcement learning …

Machine performance degradation assessment (MPDA) aims to use health indicators (HIs)
to first detect an incipient failure time (IFT) and then monotonically track machine …

By using signal processing and statistical analysis methods simultaneously, many
heterogeneous features can be produced to describe the bearings fault with more …

The incipient fault detection technology of rolling bearings is the key to ensure its normal
operation and is of great significance for most industrial processes. However, the vibration …

Performance degradation modeling is promising to construct an advanced health index (HI).
Currently, domain knowledge including monotonicity, trendabilty, and identifiability has been …

Dissolved gas analysis (DGA) is the most important tool for fault diagnosis in electric power
transformers. To improve accuracy of diagnosis, this paper proposed a new model (SDAE …

Condition monitoring (CM) of machinery is important for ensuring the reliability of industrial
processes. To adapt to the rareness of data from faulted machinery, semisupervised CM can …