Diagnosing with a hybrid fuzzy–Bayesian inference approach

Kościelny, Jan Maciej; Bartyś, Michał; Sztyber, Anna

doi:10.1016/j.engappai.2021.104345

Cited by 13 publications

(8 citation statements)

References 24 publications

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“…Different researches and studies have been proposed to deal with the diagnosis task of wind turbines using several approaches [3,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. These proposed methodologies adopt distinctive design of schemes, resulting in different properties according to the used techniques.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

Zemali¹,

Cherroun²,

Hadroug³

et al. 2023

Diagnostyka

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This paper aims to present a robust fault diagnosis structure-based observers for actuator faults in the pitch part system of the wind turbine benchmark. In this work, two linear estimators have been proposed and investigated: the Kalman filter and the Luenberger estimator for observing the output states of the pitch system in order to generate the appropriate residual between the measured positions of blades and the estimated values. An inference step as a decision block is employed to decide the existence of faults in the process, and to classify the detected faults using a predetermined threshold defined by upper and lower limits. All actuator faults in the pitch system of the horizontal wind turbine benchmark are studied and investigated. The obtained simulation results show the ability of the proposed diagnosis system to determine effectively the occurred faults in the pitch system. Estimation of the output variables is effectively realized in both situations: without and with the occurrence of faults in the studied process. A comparison between the two used observers is demonstrated.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

Zemali¹,

Cherroun²,

Hadroug³

et al. 2023

Diagnostyka

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“…Fault detection is carried out using a variety of models: analytical [1,2], neural [3,4], fuzzy [5,6], and statistical [7]. Only if the analytical models that take into account the impact of a fault on the residuals are known [2] is it possible to determine the relationship between the residuals and the faults.…”

Section: Introductionmentioning

confidence: 99%

“…In practice, models that represent the process state without any fault are usually used. In such cases, there are two options for obtaining knowledge about the impact of faults on residuals: using expert knowledge [1] or learning [8]. The learning technique requires the acquisition of experimental data not only for the normal state, but also for all faulty states that are to be recognised.…”

Section: Introductionmentioning

confidence: 99%

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Diagnostic Column Reasoning Based on Multi-Valued Evaluation of Residuals and the Elementary Symptoms Sequence

2022

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The paper concerns a significant problem in the diagnostics of industrial processes, which is the need to achieve high fault distinguishability. High distinguishability results in the generation of precise diagnoses that enable making appropriate security decisions. In the known approaches, the efforts to obtain high distinguishability are focused on the selection of an appropriate set of generated residuals. The paper presents a new method of diagnostic reasoning using the notation of faults/diagnostic signals’ relations in the form of a Fault Isolation System (FIS), which enables the use of multivalent diagnostic signals. In addition, the proposed method uses knowledge (usually incomplete) about the sequence of symptoms. Reasoning was carried out on the basis of simple, physically possible signatures, resulting from the FIS. Assumptions and a diagnostic algorithm are given. The reasoning algorithm works in a step-by-step manner, after observing further symptoms. In each reasoning step, two diagnoses are generated in parallel. A more accurate, but less certain diagnosis is formulated on the basis of the value of all diagnostic signals, and the diagnosis is less accurate, but more reliable, solely on the basis of symptoms. An example of using the method for diagnosing a set of connected liquid tanks is given. The method was compared with other reasoning methods based on columns (signatures) and, also, with row-based reasoning methods. It is shown that the proposed method allows the increase of the distinguishability of faults compared to other methods. The distinguishability grows with the knowledge of elementary symptom sequences. It is also noted that the proposed approach makes possible diagnosing not only faults, but also cyber attacks.

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“…However, this involves presuming some simplifications and undertaking some assumptions. For example, frequently, the assumption regarding the infallibility of measurement devices or the credibility of observations is adopted [10]. It is a case in both branches of the model-based diagnostics developed by the FDI and DX research communities [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Fault compensation effect in fault detection and isolation

Bartyś

2021

ACTA IMEKO

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<p class="Abstract">This paper discusses the origin and problem of the fault compensation effect. The fault compensation effect is an underrated common side effect of the fault isolation approaches developed within the Fault Detection and Isolation (FDI) community. In part, this is justified due to the relatively low probability of such an effect. On the other hand, there is a common belief that the inability to isolate faults due to this effect is the evident drawback of model-based diagnostics. This paper shows how, and under which conditions, the fault compensation effect can be identified. In this connection, the necessary and sufficient conditions for the fault compensation effect are formulated and exemplified by diagnosing a single buffer tank system in open and closed-loop arrangements. In this regard, we also show the drawbacks of a bi-valued residual evaluation for fault isolation. In contrast, we outline the advantages of a three-valued residual evaluation. This paper also brings a series of conclusions allowing for a better understanding of the fault compensation effect. In addition, we show the difference between fault compensation and fault-masking effects.</p>

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Diagnosing with a hybrid fuzzy–Bayesian inference approach

Cited by 13 publications

References 24 publications

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

Diagnostic Column Reasoning Based on Multi-Valued Evaluation of Residuals and the Elementary Symptoms Sequence

Fault compensation effect in fault detection and isolation

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