Fuzzy Fault Detection Filter Design for T–S Fuzzy Systems in the Finite-Frequency Domain

Chibani, Ali; Chadli, Mohammed; Shi, Peng; Braïek, Naceur Benhadj

doi:10.1109/tfuzz.2016.2593921

Cited by 142 publications

(57 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…, 9) and K k (k = 1, 2, 3) for the engine EQ6102. Then, the values of relation indices are calculated by Equation (4). For example, the calculating process of the relation indices η k (k = 1, 2, 3) is presented below: Using the similar calculation, the relation indices and diagnosis results of the proposed method are obtained and shown in Table 4.…”

Section: The Proposed Fault Diagnosis Methods For Misfire Fault Diagnomentioning

confidence: 99%

See 1 more Smart Citation

Logarithmic Similarity Measure between Interval-Valued Fuzzy Sets and Its Fault Diagnosis Method

2018

Information

View full text Add to dashboard Cite

Fault diagnosis is an important task for the normal operation and maintenance of equipment. In many real situations, the diagnosis data cannot provide deterministic values and are usually imprecise or uncertain. Thus, interval-valued fuzzy sets (IVFSs) are very suitable for expressing imprecise or uncertain fault information in real problems. However, existing literature scarcely deals with fault diagnosis problems, such as gasoline engines and steam turbines with IVFSs. However, the similarity measure is one of the important tools in fault diagnoses. Therefore, this paper proposes a new similarity measure of IVFSs based on logarithmic function and its fault diagnosis method for the first time. By the logarithmic similarity measure between the fault knowledge and some diagnosis-testing samples with interval-valued fuzzy information and its relation indices, we can determine the fault type and ranking order of faults corresponding to the relation indices. Then, the misfire fault diagnosis of the gasoline engine and the vibrational fault diagnosis of a turbine are presented to demonstrate the simplicity and effectiveness of the proposed diagnosis method. The fault diagnosis results of gasoline engine and steam turbine show that the proposed diagnosis method not only gives the main fault types of the gasoline engine and steam turbine but also provides useful information for multi-fault analyses and predicting future fault trends. Hence, the logarithmic similarity measure and its fault diagnosis method are main contributions in this study and they provide a useful new way for the fault diagnosis with interval-valued fuzzy information.

show abstract

Section: The Proposed Fault Diagnosis Methods For Misfire Fault Diagnomentioning

confidence: 99%

“…Fuzzy sets are suitable for solving fault diagnosis problems with uncertain information. Hence, fuzzy approaches have been widely applied to fault diagnosis processes [2][3][4][5][6]. However, it may be difficult to exactly quantify the membership degree in the fuzzy set as an exact value in the interval [0, 1].…”

Section: Introductionmentioning

confidence: 99%

Logarithmic Similarity Measure between Interval-Valued Fuzzy Sets and Its Fault Diagnosis Method

2018

Information

View full text Add to dashboard Cite

show abstract

“…where J(t) = γ −1 ψe T (t)e(t) − 2(1 − ψ)e T (t)w(t) − γw T (t)w(t), the filtering error system (5) is GUAS for any switching signal satisfying τ a ≥ ln μ α (13) and has a mixed weighted H ∞ and passivity performance index γ.…”

Section: Lemmamentioning

confidence: 99%

“…As one typical example of constrained switching, the average dwell-time (ADT) logic was proposed in the work of Hespanha and Morse. [11][12][13] Among the various methods of state estimation, the H ∞ filtering keeps attracting more attention. [8][9][10] Along with the field of switched systems, the state estimation problem of dynamic systems has also received considerable attention because of its practical applications in signal processing and control.…”

Section: Introductionmentioning

confidence: 99%

Mixed and passive filtering for linear switched systems with average dwell time

Zheng

Guo

Zhang

2017

Adaptive Control & Signal

View full text Add to dashboard Cite

In this paper, a new performance index is proposed for switched systems. The new performance index can be viewed as the mixed weighted H ∞ and passivity performance. This new performance index covers the weighted H ∞ performance and the passivity performance as special cases. Based on this new performance index, the weighted H ∞ filtering problem and the passive filtering problem of linear switched systems with unstable subsystems are solved in a unified framework. The states of the filtering error system constructed by the augmentation technique will be divergent when unstable subsystems are activated. To overcome this problem, a set of mode-dependent filters of a Luenberger-like observer type is constructed. The multiple Lyapunov function approach and the average dwell-time technique are employed to solve the mixed filtering problem.New sufficient conditions for the existence of mixed H ∞ and passive filters are developed, which ensure the filtering error system to be asymptotically stable with a prescribed mixed H ∞ and passivity performance index. Moreover, the desired mixed H ∞ and passive filters can be constructed by solving a set of linear matrix inequalities. Finally, numerical examples are given to demonstrate the applicability and advantage of the obtained results. KEYWORDS average dwell time, H ∞ filtering, linear switched systems, passive filtering, unstable subsystemsRecently, the research on switched systems has been developed because of many physical or man-made systems possessing switching features. 1,2 In general, a switched system is composed of a finite number of continuous-time or discrete-time subsystems and a switching signal. The switching signal, which orchestrates the switching among these subsystems, 316

show abstract

“…For the purpose of increasing the safety and reliability of networked controlled systems, fault diagnosis research and their applications to a wide range of industrial and commercial processes have been the subjects of intensive investigations over the past two decades [9,10,11,12]. Many fruitful results for a variety of systems have been reported [13,14,15,16,17,18,19,20,21]. …”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Fault Estimation for Stochastic Systems over Multi-Hop Relay Networks with Randomly Occurring Sensor Nonlinearities and Packet Dropouts

2018

Sensors

View full text Add to dashboard Cite

Wireless sensors have many new applications where remote estimation is essential. Considering that a remote estimator is located far away from the process and the wireless transmission distance of sensor nodes is limited, sensor nodes always forward data packets to the remote estimator through a series of relays over a multi-hop link. In this paper, we consider a network with sensor nodes and relay nodes where the relay nodes can forward the estimated values to the remote estimator. An event-triggered remote estimator of state and fault with the corresponding data-forwarding scheme is investigated for stochastic systems subject to both randomly occurring nonlinearity and randomly occurring packet dropouts governed by Bernoulli-distributed sequences to achieve a trade-off between estimation accuracy and energy consumption. Recursive Riccati-like matrix equations are established to calculate the estimator gain to minimize an upper bound of the estimator error covariance. Subsequently, a sufficient condition and data-forwarding scheme are presented under which the error covariance is mean-square bounded in the multi-hop links with random packet dropouts. Furthermore, implementation issues of the theoretical results are discussed where a new data-forwarding communication protocol is designed. Finally, the effectiveness of the proposed algorithms and communication protocol are extensively evaluated using an experimental platform that was established for performance evaluation with a sensor and two relay nodes.

show abstract

Fuzzy Fault Detection Filter Design for T–S Fuzzy Systems in the Finite-Frequency Domain

Cited by 142 publications

References 28 publications

Logarithmic Similarity Measure between Interval-Valued Fuzzy Sets and Its Fault Diagnosis Method

Logarithmic Similarity Measure between Interval-Valued Fuzzy Sets and Its Fault Diagnosis Method

Mixed and passive filtering for linear switched systems with average dwell time

Event-Triggered Fault Estimation for Stochastic Systems over Multi-Hop Relay Networks with Randomly Occurring Sensor Nonlinearities and Packet Dropouts

Contact Info

Product

Resources

About