The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …

As the concept of Industry 4.0 is introduced, artificial intelligence-based fault analysis is
attracted the corresponding community to develop effective intelligent fault diagnosis and …

Intelligent fault diagnosis based on deep learning has yielded remarkable progress for its
strong feature representation capability in recent years. Nevertheless, in engineering …

In most of the real world datasets, there is an imbalance in the number of samples belonging
to different classes. Various pattern classification problems such as fault or disease …

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary
machines. It can reduce economical losses by eliminating unexpected downtime in industry …

Since any faulty operations could directly affect the composite property, making early
prognosis is particularly crucial for complex equipment. At present, data-driven approach …

In real world problems, imbalance of data samples poses major challenge for the
classification problems as the data samples of a particular class are dominating. Problems …

This article proposes a fault diagnosis (FD) method that is based on bispectrum analysis and
a convolutional neural network (CNN) to identify bearing faults under inconsistent working …

Many deep transfer learning methods for fault diagnosis have been proposed in this decade.
Some of the existing methods focus on addressing the problem of fault data scarcity and …

As clean water can be considered among the essentials of human life, there is always a
requirement to seek its foremost and high quality. Water primarily becomes polluted due to …