A stochastic Functional Model based method for random vibration based robust fault detection under variable non–measurable operating conditions with application to railway vehicle suspensions

2020 ◽  
Vol 466 ◽  
pp. 115006 ◽  
Author(s):  
T.-C.I. Aravanis ◽  
J.S. Sakellariou ◽  
S.D. Fassois
Keyword(s):  
Fault Detection ◽  
Random Vibration ◽  
Functional Model ◽  
Operating Conditions ◽  
Railway Vehicle ◽  
Vehicle Suspensions ◽  
Model Based ◽  
Robust Fault Detection ◽  
Stochastic Functional

scholarly journals Random vibration based damage detection for a composite beam under environmental and operational variability via a stochastic Functional Model based method

2018 ◽  
Vol 188 ◽  
pp. 01003 ◽  
Author(s):  
Tryfon-Chrysovalantis Aravanis ◽  
Spiros Kolovos ◽  
John Sakellariou ◽  
Spilios Fassois
Keyword(s):  
Damage Detection ◽  
Composite Beam ◽  
Random Vibration ◽  
State Of The Art ◽  
Functional Model ◽  
Principal Component ◽  
Operating Conditions ◽  
Baseline Phase ◽  
Model Based ◽  
Stochastic Functional

The problem of random vibration response based damage detection for a composite beam under non-measurable environmental and operational variability, presently temperature and tightening torque, is considered via a Functional Model based method. The method is based on proper representation of the healthy structural dynamics under any environmental/operating conditions via a data based Functional Model obtained in the method’s baseline phase and used to define a ‘healthy subspace’. Damage detection is, in the method’s inspection phase, achieved by examining whether or not the current dynamics belongs to the healthy subspace. The experimental results obtained for damage detection on a composite beam indicate excellent detection performance, with correct detection rate of 100% for false alarm rate as small as 1%. The superiority of the proposed method is confirmed via comparisons with a state-of-the-art Principal Component Analysis based method.

A Model-Based Online Fault Detection Method for Air Handling Units of Real Office Buildings

2011 ◽  
Vol 90-93 ◽  
pp. 3061-3067
Author(s):  
Hai Tao Wang ◽  
You Ming Chen ◽  
Cary W.H. Chan ◽  
Jian Ying Qin
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Office Buildings ◽  
Model Parameters ◽  
Time Data ◽  
Model Based ◽  
Air Handling Units ◽  
Detection And Diagnosis

The increasing performance demands and the growing complexity of heating, ventilation and air conditioning (HVAC) systems have created a need for automated fault detection and diagnosis (FDD) tools. Cost-effective fault detection and diagnosis method is critical to develop FDD tools. To this end, this paper presents a model-based online fault detection method for air handling units (AHU) of real office buildings. The model parameters are periodically adjusted by a genetic algorithm-based optimization method to reduce the residual between measured and predicted data, so high modeling accuracy is assured. If the residual between measured and estimated performance data exceeds preset thresholds, it means the occurrence of faults or abnormalities in the air handling unit system. In addition, an online adaptive scheme is developed to estimate and update the thresholds, which vary with system operating conditions. The model-based fault detection method needs no additional instrumentation in implementation and can be easily integrated with existing energy management and control systems (EMCS). The fault detection method was tested and validated using in real time data collected from a real office building.

scholarly journals Neurofuzzy Approach to Fault Detection of Nonlinear Systems

1999 ◽  
Vol 3 (6) ◽  
pp. 524-531
Author(s):  
Jinglu Hu ◽  
◽  
Kotaro Hirasawa ◽  
Kousuke Kumamaru ◽  
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Linear Systems ◽  
Linear Model ◽  
Basis Function ◽  
Linear Models ◽  
Detection Method ◽  
Local Model ◽  
Model Based ◽  
Robust Fault Detection

This paper proposes a neurofuzzy approach to fault detection in linear systems. The system diagnosed is described by using a neurofuzzy model called LimNet that consists of a linear model and multiple local linear models with interpolation of a "fuzzy basis function". Fault detection is considered in two cases: when faults occur in the linear model part, a KDI-based robust fault detection is applied, where a multi-local-model part is treated as error due to nonlinear undermodeling; when faults occur in the multi-local-model part, a multi-model based fault detection method is developed, in which the identified LimNet is interpreted as several local ARMAX models, and KDI is used as an index to discriminate between each local model and its reference. This paper mainly concentrates discussions on multi-model based fault detection.

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