Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

Abstract

Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this include: (a) access to the machines on site is sometimes impracticable, and (b) the environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite theoretically sound findings on developing intelligent solutions for machine condition-based monitoring, few commercial tools exist in the market that can be readily used. This paper examines the development of an intelligent fault recognition and monitoring system (Melvin I), which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor to extract characteristic vibration signals of ten key performance indicators (KPIs). A 3-layer neural network is designed to recognize and classify faults based on a pre-determined set of KPIs. The system implemented in the laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Results show that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.

Rotating machinery fault identification using model-based and data-based techniques integration.

“…In fact, in the past 30 years, PCA, Artificial Neural Networks (ANN), Fuzzy Logic (FL), Bayesian Network (BN), and PLS were the most usual methods find in FDD literature. On the other hand, BN, Hidden Markov's Methods (HMM), Gaussian Mixture Model (GMM), ICA, and Support Vector Machine (SVM) have gained more attention in the last 5 years Ngolah et al (2011),. for example, detailed the monitoring and diagnosis of common faults, such as rub and looseness, using ANN.…”