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In this paper, a model-based identification method for multiple faults is presented.

The fault diagnosis model of multiple faults is given based on evidence theory.

This is due to the fact that the estimates of EKF are often biased when the occurrence of multiple faults is possible.

However, the multi-fault diagnosis in gearboxes is a challengeable problem because the signal measured from the gearbox with multiple faults is complex and non-stationary.

A fault isolation filter (FIF) for fault detection of multiple input multiple output (MIMO) networked control systems (NCSs) with multiple faults is then parameterized.

Instead, in real machines, the case of multiple faults is quite common: the simultaneous presence of a bow (due to several different causes) and an unbalance or a coupling misalignment occurs often in rotor systems.

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Similar(51)

Finally, simulations of traction engine multiple faults are conducted to illustrate the proposed method.

The magnitude and occurrence time of the multiple faults are unknown.

Finally, in guidance of elaborately designed fault related sensitive indexes, latent fault feature subspaces can be adaptively recognized and multiple faults are diagnosed simultaneously.

In order to enhance generalization level of data samples, multiple faults are simulated in data producing procedure to consider N − k (k ≤ 3) scenarios.

Under the assumption that the magnitude and occurrence time of multiple faults are unknown, we first design a delay-independent fault detection scheme with a detection threshold for time-delay systems and analyze the fault detectability.

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