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The rule-based fault diagnosis method presented is efficient to isolate multiple faults of air handling units.
Many practical applications of system diagnosis require the credible identification of multiple faults of nonlinear components and sensors in quantitative measures.
In practice, due to harsh working environment and heavy working loads, multiple faults of an aircraft hydraulic pump may occur simultaneously after long time operations.
Furthermore, the rule-based fault diagnosis method is extended to isolate simultaneous multiple faults of VAV air handling units in the mechanical cooling operation mode.
In this paper, a robust fault detection and diagnosis strategy is presented for multiple faults of air handling units by using the residual-based exponentially weighted moving average (EWMA) control chart method and the rule-based fault diagnosis method.
It is assumed that (i) nonlinearities and multiple faults of follower models and their bounds are unknown, and (ii) the time-varying posture information of the leader robot is accessible to only a small fraction of follower robots under directed networks.
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Therefore, new method needs to be developed to realize effective diagnosis of simultaneous multiple faults on aircraft hydraulic pump.
We emphasize it is still a difficult problem to use an unsupervised learning method to diagnose multiple faults for down-hole conditions of sucker rod pumping systems.
Based on the model-based diagnostic approaches, Bachschmid and Pennacchi [9] and Platz et al. [10] studied multiple faults identification of rotor system.
In this paper, a novel method for multiple faults identification of nonlinear systems based on T-S fuzzy model and parameter estimation is presented.
In this paper, the wavelet packet transform (WPT) is developed and incorporated into the MSTSR method to overcome its shortcomings and to further enhance its capability in multiple faults detection of bearings.
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