A Survey of Active Fault Diagnosis Methods

Punčochář, Ivo; Škach, Jan

doi:10.1016/j.ifacol.2018.09.726

Cited by 39 publications

(12 citation statements)

References 61 publications

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“…Suppose that the system we want to monitor can be described by a nominal model P 0 and N known possible faulty models, each of which is described by an LTI system of the same order as P 0 . Now, let each of the faulty models be denoted P i for i ∈ IN [1,N ] and have dynamics given by (2) where…”

Section: Problem Formulationmentioning

confidence: 99%

“…Fault diagnosis methods can be separated into two broad classes [1]: the first group of methods includes passive fault diagnosis techniques, where input/output data is used to make a decision about the health status of the system. In the second group, represented by so-called active methods (see [2] for a recent survey), the control input is optimized to help diagnose a fault; these methods are termed active fault diagnosis (AFD). A major shortcoming of traditional passive fault diagnosis techniques is that feedback controllers can compensate the effect of faults, thus hiding their presence or making them difficult to be distinguished.…”

Section: Introductionmentioning

confidence: 99%

“…This may result in undesirable delays in in detecting and/or isolating faults, possibly leading to more severe consequences or propagation to major failures in the system. As explained in [2], many different methods and scenarios have been proposed under the umbrella of AFD, considering deterministic [3], [4] or stochastic uncertainties [5], [6], hybrid stochastic-deterministic approaches [7], [8], and finite or infinite AFD auxiliary control sequences [9].…”

Section: Introductionmentioning

confidence: 99%

“…As pointed out in [2], the implementation of active fault diagnosis techniques in practical settings is still limited, mainly due to the high computational complexity that these methods require. Even though some approaches have been suggested to alleviate the computational complexity (see for example [15]), this drawback hinders the applicability of AFD to even low-dimensional systems or networks of few interconnected systems [16].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Active Fault Diagnosis via Reachable Set Separation using Interval Methods

Tsiakkas

Sopasakis

Boem

et al. 2019

2019 18th European Control Conference (ECC)

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This paper presents an optimization-based approach for active fault isolation in linear time invariant systems subject to additive disturbances which are bounded in intervals. We model the evolution of the system states and outputs using interval dynamics with the input signals also bounded within given intervals. The proposed approach is based on determining a sequence of control actions to minimize a function which quantifies the overlap of output intervals, while taking into account the prescribed input bounds. The resulting optimization problem is nonconvex, yet the cost function has Lipschitzcontinuous gradient and the constraints are simple, which allows us to solve them efficiently. A numerical example is used to illustrate the implementation and suitability of the proposed optimization approach for active fault isolation.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Active Fault Diagnosis via Reachable Set Separation using Interval Methods

Tsiakkas

Sopasakis

Boem

et al. 2019

2019 18th European Control Conference (ECC)

View full text Add to dashboard Cite

show abstract

“…Active FD methods mainly include deterministic and probabilistic methods depending on the underlying assumptions regarding system noise and disturbance. 11 The deterministic methods assume that the noise and disturbance can be modeled as an unknown but bounded signal. Examples of such methods are the integrated controller and detector design method and the guaranteed FD approach.…”

Section: Introductionmentioning

confidence: 99%

Active fault detection based on set‐membership approach for uncertain discrete‐time systems

Wang

Shi

Zhou

et al. 2020

Intl J Robust & Nonlinear

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Active fault detection facilitates determination of the fault characteristics by injecting proper auxiliary input signals into the system. This article proposes an observer-based on-line active fault detection method for discrete-time systems with bounded uncertainties. First, the output including disturbances, measurement noise and interval uncertainties at each sample time is enclosed in a zonotope. In order to reduce the conservativeness in the fault detection process, a zonotopic observer is designed to estimate the system states allowing to generate the output zonotopes. Then, a proper auxiliary input signal is designed to separate the output zonotopes of the faulty model from the healthy model that is injected into the system to facilitate the detection of small fault . Since the auxiliary input signal generation leads to a nonconvex optimization problem, it is transformed into a mixed integer quadratic programming problem. Finally, a case study based on a DC motor is used to show the effectiveness of the proposed method.

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Multivariate alarm systems to recognize rare unpostulated abnormal events

Sudarshan,

Seider,

Patel

et al. 2023

AIChE Journal

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Most chemical and manufacturing plants have safety/reliability systems in place that are well equipped to handle commonly occurring postulated abnormal events, but often prove to be ineffective in predicting highly unanticipated and randomly occurring unpostulated abnormal events. In this paper, an advisory system is developed to analyze and recognize such events, consisting of novel, multivariate alarm systems and response actions introduced using process modeling and path‐sampling for unpostulated abnormal events. It augments existing safety/reliability systems, suggesting actions when unanticipated abnormal events are approached. Forward‐flux sampling (FFS), developed to discover rare molecular dynamics pathways, is applied. With an approximate process model, for an exothermic continuous‐stirred tank reactor using a perturbed feed concentration, the FFS algorithm is applied to identify rare trajectories between high‐conversion and low‐conversion steady‐states, with key process variables saved at various “crossing points.” Then, committer probabilities, , are computed at each crossing point, yielding a mathematical model that expresses as a function of the key process variables; that is, the reactor temperature (T), cooling‐water flow rate (), and cooling‐water temperature (), selected as choices for the primary alarm variables. For these, alarm thresholds; that is, L—low, LL—low low, and LLL—low low low, are suggested by computing their critical ranges, given the ranges for every alarm threshold.

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A Survey of Active Fault Diagnosis Methods

Cited by 39 publications

References 61 publications

Active Fault Diagnosis via Reachable Set Separation using Interval Methods

Active Fault Diagnosis via Reachable Set Separation using Interval Methods

Active fault detection based on set‐membership approach for uncertain discrete‐time systems

Multivariate alarm systems to recognize rare unpostulated abnormal events

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