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A case study on multisensor data fusion for imbalance diagnosis of rotating machinery. (English) Zbl 0972.68543

(AI EDAM) Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, No. 3, 203-210 (2001).
Summary: Techniques for machine mondition monitoring and diagnostics are gaining acceptance in various industrial sectors. They have proved to be effective in predictive or proactive maintenance and quality control. Along with the fast development of computer and sensing technologies, sensors are being increasingly used to monitor machine status. In recent years, the fusion of multisensor data has been applied to diagnose machine faults. In this study, multisensors are used to collect signals of rotating imbalance vibration of a test rig. The characteristic features of each vibration signal are extracted with an auto-regressive model. Data fusion is then implemented with a cascade-correlation neural network. The results elearly show that multisensor data-fusion-based diagnostics outperforms the single sensor diagnostics with statistical significance.

MSC:

68U99 Computing methodologies and applications
62P30 Applications of statistics in engineering and industry; control charts
68T99 Artificial intelligence
68T05 Learning and adaptive systems in artificial intelligence
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