Adaptive fault diagnosis in rotating machines using indicators selection

• Adaptive algorithm for fault diagnosis in industrial rotating machines is designed.
• Experiments were conducted on industrial rotating machines in various conditions.
• Hybrid approach of indicators selection was used for the algorithm development.
• The results were compared with the J48 decision tree algorithm.
• Indicators reselection is crucial step for reliable rotating machines monitoring.

Over the past two decades, condition monitoring and faults diagnosis in rotating machinery have been widely studied and reported. In the present paper an algorithm for fault diagnosis in industrial rotating machines facing new operating conditions emergence is developed on the basis of input indicators, extracted from vibrations spectrums. Indicators selection is used to improve diagnosis performances by the help of a hybrid approach using several selection criteria and different classifiers. To validate the performances of this algorithm, experimental tests were conducted on two industrial systems with various operating conditions. The results have proved the effectiveness of the developed algorithm compared to the “J48 decision tree” and also reveal the need to re-select the indicators for reliable monitoring of working conditions.