Rotating machines are frequently subject to a wide range of rough conditions, resulting in
mechanical failures and performance degradation. Thus, it is important to apply proper …

Condition monitoring of electrical Rotating Machines (RM) serves in structural changes
detection during machine's operation. However, the frequent fault occurrence reduces the …

This work proposes a simpler automatic fault classifier that uses multi-layer perceptron
(MLP) to identify faults in rotating machines. For classification, only statistical features and …

It is incredibly difficult to build a data-driven machine learning model for the automatic
detection of defects in rotating machinery. The existing techniques, based on machine …

Accurate and efficient rotating machinery fault diagnosis is crucial for industries to guarantee
the productivity and reduce the maintenance cost. This paper systematically proposes a new …

Intelligent fault diagnosis techniques play an important role in improving the abilities of
automated monitoring, inference, and decision making for the repair and maintenance of …

Early fault diagnosis of rotating machinery is crucial in the industry. The network parameters
of the traditional deep learning-based fault diagnosis method are optimized only by the …

Fault diagnosis plays an essential role in reducing the maintenance costs of rotating
machinery manufacturing systems. In many real applications of fault detection and …

Fault identification is a vital task to ensure the integrity and reliability of rotating machinery.
The vibration signals produced by the defective system components typically bear a …

This work presents a multi-fault classification system using artificial neural network (ANN) to
distinguish between different faults in rotating machines automatically. Rotation frequency …