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Based on the available data on the shop floor, the potential fault/error patterns, referring to machining errors, machine faults and maintenance states, are mined for unsuitable scheduling arrangements before machining as well as upcoming errors during machining.
normal behaviour, transient operation, and characteristics of emerging machine faults.
Moreover, most frequency spectrums have similar characteristics, which make misjudgements in detecting machine faults [8].
This pseudo-phase portrait is then used to extract qualitative features of machine faults.
Online induction machine faults diagnosis is a concern to guarantee the overall production process efficiency.
These merits are presented in this paper and applied to diagnose three types of machine faults.
Similar(46)
Experiments are conducted using Spectra Quest machine fault simulator with different prefabricated faulted bearings.
Vibration-based analysis is an important technique in machine fault diagnosis.
The paper proposes a new methodology of machine fault detection for industrial gas turbine (IGT) systems.
Many statistical indicators were proposed for machine fault diagnosis [24, 25, 26, 27].
Wavelet transform has been widely used for vibration-based machine fault diagnosis.
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