scholarly journals A Multihypothesis Sequential Probability Test for Fault Detection and Identification of Vehicles' Ultrasonic Parking Sensors

2011 ◽  
Vol 2011 ◽  
pp. 1-11
Author(s):  
Mamoun F. Abdel-Hafez
Keyword(s):  
Fault Detection ◽  
Measurement Noise ◽  
Experimental Results ◽  
Identification Algorithm ◽  
Ultrasonic Sensors ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Correct Hypothesis ◽  
Sequential Probability

This paper presents a sequential fault detection and identification algorithm for detecting a fault in a vehicle's ultrasonic parking sensors. The algorithm identifies a bias fault in any of the ultrasonic sensors by computing the probability of having that bias fault given a carefully constructed measurement residual that is only a function of the measurement noise and the possible measurement fault. A set of bias hypotheses is assumed and initially given equal alarm probability. It is assumed that only one sensor will acquire a bias at any given time. Once the probability of a hypothesis approaches 1, that hypothesis is declared as the correct hypothesis and the bias associated with the hypothesis is removed from the sensors' reading. The accuracy and convergence characteristics of the proposed algorithm are verified using experimental results. This study is essential to ensure accurate operation of vehicle's ultrasonic parking sensors.

Fault Detection and Identification in an IM270 Gas Turbine Using Measurements for Engine Control

2003 ◽  
Author(s):  
Masahiro Kurosaki ◽  
Tadashi Morioka ◽  
Kosuke Ebina ◽  
Masatoshi Maruyama ◽  
Tomoshige Yasuda ◽  
...  
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Identification Algorithm ◽  
Gradual Change ◽  
Engine Control ◽  
Unknown Parameters ◽  
Engine Model ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Parameter Deviation

A unique fault detection and identification algorithm using measurements for engine control use is presented. The algorithm detects an engine fault and identifies the associated component, using a gas path analysis technique with a detailed nonlinear engine model. The algorithm is intended to detect step-like changes in component performance rather than gradual change of all components. Since simultaneous multiple faults are unlikely, a single component fault is assumed, which reduces the number of unknown parameters to less than two. By setting the number of adjustable parameters to that of the available measurements, the parameters are computed using an engine model. After computing all of the six possible combinations of adjustable parameters, the average magnitude of the parameter deviation vectors is used to detect an engine fault. Component performance deviation (efficiency and flow rate) is represented by a magnitude and a phase. The phase is selected to minimize the error of matrices consisting of normalized adjustable parameter deviation vectors. Then the magnitude is computed by the average magnitude ratio of the vectors. Since the algorithm is simple, it is easily applied to newly developed engines. A fault detection and identification program was specifically developed for IM270 engine, a single shaft gas turbine with 2MW output capacity. By utilizing operational data obtained at a remote monitoring center, the algorithm was able to quantitatively identify the compressor and the turbine performance deviation. Although the algorithm correctly identifies the turbine as the faulty component, there remains some ambiguity. Analysis of linear dependency of the measurement deviation vectors shows that identification capability varies with phase. There are several phases where identification is impossible in the current IM270 sensor system.

Fault Detection and Identification in an IM270 Gas Turbine Using Measurements for Engine Control

2004 ◽  
Vol 126 (4) ◽  
pp. 726-732 ◽  
Author(s):  
Masahiro Kurosaki ◽  
Tadashi Morioka ◽  
Kosuke Ebina ◽  
Masatoshi Maruyama ◽  
Tomoshige Yasuda ◽  
...  
Keyword(s):  
Fault Detection ◽  
Identification Algorithm ◽  
Gradual Change ◽  
Engine Control ◽  
Engine Model ◽  
Turbine Performance ◽  
Detection And Identification ◽  
Analysis Technique ◽  
Fault Detection And Identification ◽  
Operational Data

A unique fault detection and identification algorithm using measurements for engine control use is presented. The algorithm detects an engine fault and identifies the associated component, using a gas path analysis technique with a detailed nonlinear engine model. The algorithm is intended to detect steplike changes in component performance rather than gradual change of all components. Component performance deviation (efficiency and flow rate) is represented by a magnitude and a phase. The phase is selected to minimize the error of evaluation matrices. Then the magnitude is computed. By utilizing operational data of the IM270 engine, the compressor and the turbine performance deviation was quantitatively identified.

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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