Key Techniques in Intelligent Predictive Maintenance (IPdM) – A Framework of Intelligent Faults Diagnosis and Prognosis System (IFDaPS)


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Intelligent predictive maintenance (IPdM) is a maintenance strategy that makes maintenance decisions automatically and dynamically based on Artificial Intelligence and Data mining techniques through condition monitoring of machines, equipment and production processes. IPdM system consists of the following main modules: sensor and data acquisition, signal and data processing, feature extractions, maintenance decision-making, key performance indicators, maintenance scheduling optimization and feedback control and compensation. Among them, the most important part of IPdM is maintenance decision-making, which includes diagnostics and prognostics. This paper proposes a framework of intelligent faults diagnosis and prognosis system (IFDaPS) and discuss some key technologies for implement IPdM policy in manufacturing and industries. A case study focus on the vibration signals collected from the sensors mounted on a pressure blower for critical components monitoring. We decompose the pre-processed signals into several signals using Wavelet Packet Decomposition (WPD), and then the signals are transformed to frequency domain using Fast Fourier Transform (FFT). The features extracted from frequency domain are used to train Artificial Neural Network (ANN). Trained ANN model is able to identify the fault of the components and predict its Remaining Useful Life (RUL). The case study demonstrates how to implement the proposed framework and intelligent technologies for IPdM and the result indicates its higher efficiency and effectiveness comparing to traditional methods.



Edited by:

Kesheng Wang, Jan Ola Strandhagen and Dawei Tu




K. S. Wang "Key Techniques in Intelligent Predictive Maintenance (IPdM) – A Framework of Intelligent Faults Diagnosis and Prognosis System (IFDaPS)", Advanced Materials Research, Vol. 1039, pp. 490-505, 2014

Online since:

October 2014





* - Corresponding Author

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