A Functional Modelling Based Methodology for Testing the Predictions of Fault Detection and Identification Systems

2016 ◽  
Author(s):  
Nikolaos Papakonstantinou ◽  
Scott Proper ◽  
Douglas L. Van Bossuyt ◽  
Bryan O’Halloran ◽  
Irem Y. Tumer
Keyword(s):  
Fault Detection ◽  
Nuclear Power ◽  
Past Research ◽  
System A ◽  
Detection And Identification ◽  
Incorrect Prediction ◽  
Fault Detection And Identification ◽  
Analytical Proof ◽  
Critical Process

Fault detection and identification (FDI) systems, which are based on data mining and artificial intelligence techniques, cannot guarantee a perfect success rate or provide analytical proof for their predictions. This characteristic is problematic when such an FDI system is monitoring a safety-critical process. In these cases, the predictions of the FDI system need to be verified by other means, such as tests on the process, to increase trust in the diagnosis. This paper contributes an extension of the Hierarchical Functional Fault Detection and Identification (HFFDI) system, a combination of a plant-wide and multiple function-specific FDI modules, developed in past research. A test preparation and test-based verification phase is added to the HFFDI methodology. The functional decomposition of the process and the type of the faulty components guides the preparation of specific tests for every fault to be identifiable by the HFFDI system. These tests have the potential to confirm or disprove the existence of the fault(s) in the target process. The target is minor automation faults in redundant systems of the monitored process. The proposed extension of the HFFDI system is applied to a case study of a generic Nuclear Power Plant model. Two HFFDI predictions are tested (a successful and an incorrect prediction) in single fault scenarios and one prediction is tested in a in a two fault scenario. The results of the case study show that the testing phase introduced in this paper is able to confirm correct fault predictions and reject incorrect fault predictions, thus the HFFDI extension presented here improves the confidence of the HFFDI output.

A Plant-Wide and Function-Specific Hierarchical Functional Fault Detection and Identification (HFFDI) System for Multiple Fault Scenarios on Complex Systems

2015 ◽  
Author(s):  
Nikolaos Papakonstantinou ◽  
Scott Proper ◽  
Bryan O’Halloran ◽  
Irem Y. Tumer
Keyword(s):  
Fault Detection ◽  
Complex Systems ◽  
Detection Methods ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Single Fault ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
And Function

The development of Fault Detection and Identification (FDI) systems for complex mechatronic systems is a challenging process. Many quantitative and qualitative fault detection methods have been proposed in past literature. Few methods address multiple faults, instead an emphasis is placed on accurately proving a single fault exists. The omission of multiple faults regulates the capability of most fault detection methods. The Functional Failure Identification and Propagation (FFIP) framework has been utilized in past research for various applications related to fault propagation in complex systems. In this paper a Hierarchical Functional Fault Detection and Identification (HFFDI) system is proposed. The development of the HFFDI system is based on machine learning techniques, commonly used as a basis for FDI systems, and the functional system decomposition of the FFIP framework. The HFFDI is composed of a plant-wide FDI system and function-specific FDI systems. The HFFDI aims at fault identification in multiple fault scenarios using single fault data sets, when faults happen in different system functions. The methodology is applied to a case study of a generic nuclear power plant with 17 system functions. Compared with a plant-wide FDI system, in multiple fault scenarios the HFFDI gave better results for identifying one fault and also was able to identify more than one faults. The case study results show that in two fault scenarios the HFFDI was able to identify one of the faults with 79% accuracy and both faults with 13% accuracy. In three fault scenarios the HFFDI was able to identify one of the faults with 69% accuracy, two faults with 22% accuracy and all three faults with 1% accuracy.

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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