scholarly journals A Novel Fault Detection and Identification Framework for Rotating Machinery Using Residual Current Spectrum

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5865
Author(s):  
Widagdo Purbowaskito ◽  
Chen-Yang Lan ◽  
Kenny Fuh
Keyword(s):  
Fault Detection ◽  
State Space Model ◽  
Rotating Machinery ◽  
Steady State Condition ◽  
Residual Spectrum ◽  
Current Spectrum ◽  
Residual Signal ◽  
Detection And Identification ◽  
Study Results ◽  
Fault Detection And Identification

A novel framework of model-based fault detection and identification (MFDI) for induction motor (IM)-driven rotating machinery (RM) is proposed in this study. A data-driven subspace identification (SID) algorithm is employed to obtain the IM state-space model from the voltage and current signals in a quasi-steady-state condition. This study aims to improve the frequency–domain fault detection and identification (FDI) by replacing the current signal with a residual signal where a thresholding method is applied to the residual signal. Through the residual spectrum and threshold comparison, a binary decision is made to find fault signatures in the spectrum. The statistical Q-function is used to generate the fault frequency band to distinguish between the fault signature and the noise signature. The experiment in this study is performed on a wastewater pump in an existing industrial facility to verify the proposed FDI. Two faulty conditions with mathematically known and mathematically unknown faulty signatures are experimented with and diagnosed. The study results present that the residual spectrum demonstrated to be more sensitive to fault signatures compare to the current spectrum. The proposed FDI has successfully shown to identify the fault signatures even for the mathematically unknown faulty signatures.

scholarly journals A Novel Method of Fault Detection and Identification in a Tightly Coupled, INS/GNSS-Integrated System

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2922
Author(s):  
Fan Zhang ◽  
Ye Wang ◽  
Yanbin Gao
Keyword(s):  
Fault Detection ◽  
Parametric Bootstrap ◽  
Integrated System ◽  
Range Data ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Multiple Faults ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Tightly Coupled

Fault detection and identification are vital for guaranteeing the precision and reliability of tightly coupled inertial navigation system (INS)/global navigation satellite system (GNSS)-integrated navigation systems. A variance shift outlier model (VSOM) was employed to detect faults in the raw pseudo-range data in this paper. The measurements were partially excluded or included in the estimation process depending on the size of the associated shift in the variance. As an objective measure, likelihood ratio and score test statistics were used to determine whether the measurements inflated variance and were deemed to be faulty. The VSOM is appealing because the down-weighting of faulty measurements with the proper weighting factors in the analysis automatically becomes part of the estimation procedure instead of deletion. A parametric bootstrap procedure for significance assessment and multiple testing to identify faults in the VSOM is proposed. The results show that VSOM was validated through field tests, and it works well when single or multiple faults exist in GNSS measurements.

scholarly journals Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and Identification

2009 ◽  
Vol 19 (2) ◽  
pp. 327-335 ◽  
Author(s):  
Tomasz Barszcz
Keyword(s):  
Fault Detection ◽  
Adaptive Filter ◽  
Complex Structure ◽  
Reference Signal ◽  
The Self ◽  
Noise Component ◽  
Vibration Signals ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Self Adaptive

Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and IdentificationThe paper investigates the possibility of decomposing vibration signals into deterministic and nondeterministic parts, based on the Wold theorem. A short description of the theory of adaptive filters is presented. When an adaptive filter uses the delayed version of the input signal as the reference signal, it is possible to divide the signal into a deterministic (gear and shaft related) part and a nondeterministic (noise and rolling bearings) part. The idea of the self-adaptive filter (in the literature referred to as SANC or ALE) is presented and its most important features are discussed. The flowchart of the Matlab-based SANC algorithm is also presented. In practice, bearing fault signals are in fact nondeterministic components, due to a little jitter in their fundamental period. This phenomenon is illustrated using a simple example. The paper proposes a simulation of a signal containing deterministic and nondeterministic components. The self-adaptive filter is then applied—first to the simulated data. Next, the filter is applied to a real vibration signal from a wind turbine with an outer race fault. The necessity of resampling the real signal is discussed. The signal from an actual source has a more complex structure and contains a significant noise component, which requires additional demodulation of the decomposed signal. For both types of signals the proposed SANC filter shows a very good ability to decompose the signal.

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