Fault diagnosis based on the quality effect of learning algorithm for manufacturing systems

Abstract

Fault diagnosis is becoming an important issue in industrial environment, and the accurate diagnosis is the most significant part in fault handling. This article discusses a fault detection and diagnosis problem for manufacturing systems taking into account rapid detection and speed performances of fault isolation with minimum ambiguity. However, in many complex real plants, it may not be possible to discover accurately the causes of probable faults. The accuracy of fault or fault detection by the traditional approaches is not adequate. Considering the quality effect of the learning algorithm, a new hybrid neural network approach is developed using the integration of a regression task for classification accuracy. Two models of neural networks: gradient descent and momentum & adaptive learning rate and Levenberg–Marquardt are investigated and compared. The performance of the proposed approach is evaluated using mean square error, convergence speed, and classification accuracy. The case study and experimental results are presented and discussed. A comparison with the Levenberg–Marquardt regression approach shows the importance of considering the proposed learning algorithm quality in the fault detection and diagnosis problem compared with those reported in the literature.

An accurate and efficient machine fault diagnosis approach using a recurring broad learning model

“…[17][18][19][20][21] It is an effective solution to overcome such drawbacks mentioned above because of their powerful automatic feature learning ability and improved classification accuracy. DL-based methods have been successfully applied to mechanical fault diagnosis, [22][23][24][25][26][27][28][29][30][31][32][33] but there still existed some problems. For example, (1) too many network parameters with increasing of hidden layers, it lead to time consuming hyperparameter tuning process (2) deeper layers produce more complicated network structure, (3) the DL model requires a large amount of labeled data for training, but it is impossible in many real cases.…”

Federated Learning Based Fault Diagnosis Driven by Intra-Client Imbalance Degree

“…As a key component of a motor, fault diagnosis research on rolling bearings plays an important role in ensuring the safe and stable operation of the motor [ 1 , 2 , 3 ]. As an effective data-driven fault diagnosis approach, deep learning is not limited by a precise physical model or adequate expert knowledge and can automatically extract fault features from raw data [ 4 , 5 , 6 ].…”

Research on federated learning method for fault diagnosis in multiple working conditions

“…As an indispensable core component in the connection and transmission chain of mechanical equipment, rolling bearings play an important role in aerospace, electric power, metallurgy, and other industrial fields [1] . Mechanical equipment working in a complex environment is prone to a higher fault rate related to rolling bearings.…”