Optimal Zonotopic Kalman Filter-based State Estimation and Fault-diagnosis Algorithm for Linear Discrete-time System with Time Delay

Liu, Zi-Xing; Wang, Ziyun; Wang, Yan; Ji, Zhicheng

doi:10.1007/s12555-021-0267-x

Cited by 5 publications

(4 citation statements)

References 39 publications

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“…The two famous fault prediction tools are the Kalman filter and the Alpha-Beta-Gamma tracking filter, which are often used in aerospace PHM and many other fields [43][44][45][46][47]92]. In hybrid approaches, both model-based techniques and datadriven methods are amalgamated and used for fault prognosis and diagnosis [93][94][95][96][97]. The traditional method of PHM is based on analytical models that draw on physical principles and subject-matter expertise.…”

Section: Phm Methodologiesmentioning

confidence: 99%

Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review

2023

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The availability of computational power in the domain of Prognostics and Health Management (PHM) with deep learning (DL) applications has attracted researchers worldwide. Industrial robots are the prime mover of modern industry. Industrial robots comprise multiple forms of rotating machinery, like servo motors and numerous gears. Thus, the PHM of the rotating components of industrial robots is crucial to minimize the downtime in the industries. In recent times, deep learning has proved its mettle in different areas, like bio-medical, image recognition, speech recognition, and many more. PHM with DL applications is a rapidly growing field. It has helped achieve a better understanding of the different condition monitoring signals, like vibration, current, temperature, acoustic emission, partial discharge, and pressure. Most current review articles are component- (or system-)specific and have not been updated to reflect the new deep learning approaches. Also, a unified review paper for PHM strategies for industrial robots and their rotating machinery with DL applications has not previously been presented. This paper presents a review of the PHM strategies with various DL algorithms for industrial robots and rotating machinery, along with brief theoretical aspects of the algorithms. This paper presents a trend of the up-to-date advancements in PHM approaches using DL algorithms. Also, the restrictions and challenges associated with the available PHM approaches are discussed, paving the way for future studies.

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Section: Phm Methodologiesmentioning

confidence: 99%

Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review

2023

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“…Remark 3. Recalling (8), when the value of W is selected appropriately, for example, the row vectors of the gain matrix W can be designed as the left eigenvectors corresponding to the zero eigenvalue of DTFM G d (e j𝜔 d T s ), we have WG d (z)| z=e j𝜔 d T s = 0, thus WG d (z)d(z) = 0, and the disturbance d(z) is decoupled from the fault detection residual (r)(k). In a summary, when the observer is convergent, that is, |𝜆(Ã)| < 1 and ( 7)-( 9) are satisfied, WG d (z)d(z) in (10) tends to zero, which means the fault detection residual r(k) is fully decoupled from the disturbance d(k).…”

Section: Add-gpio Designmentioning

confidence: 99%

“…Since the precursory work of Isermann, 1 Ye et al 2 model‐based fault detection (FD) has been a promising research area for the last few decades 3 . In particular, observer‐based fault detection methods have been intensively studied 4‐6 and numerous fault detection methods based on classical observers have been proposed till now, such as Luenberger observer, 7 Kalman filter, 8 Unknown Input Observer (UIO), 9 sliding‐mode observer, 10 proportional‐integral observer, 11 and recently, the generalized proportional‐integral observer (GPIO) 12,13 . The basic idea is to construct an appropriate observer to generate a favourite fault‐sensitive residual signal, so that a fault can be relatively easily detected by evaluating the residual's features, such as change patterns and magnitudes.…”

Section: Introductionmentioning

confidence: 99%

“…minimizing the gain from disturbances to residuals) and sensitivity to faults (e.g., maximizing the gain from fault to residuals) 23 by adjusting the poles shared by the DTFM and FTFM. This is a common issue in conventional proportional observers 7‐11 or filters designed by the

{H}_i/{H}_{\infty }

optimization technique, 20,21w hich is a challenge worth investigating. In addition, the UIO technique 22 also fails when the DTFM is equal to the FTFM.…”

Section: Introductionmentioning

confidence: 99%

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A generalized proportional‐integral observer for fault detection: An approximate input disturbance decoupling approach

Dai

Cui

et al. 2023

Adaptive Control & Signal

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Summary This article proposes a generalized PI observer with approximate disturbance decoupling (ADD‐GPIO) for detecting incipient actuator faults of a system subject to periodic input disturbances. Robustness to disturbances and sensitivity to faults is achieved through dynamic configuration of the disturbance transmission zeros and pole‐optimization respectively. By incorporating the v$$ v $$‐gap metric with Hprefix−$$ {H}_{-} $$ index, a novel interpretable fault sensitivity optimization objective function is proposed. It is the first time in the fault detection observer design that the difference between the transfer function matrices (TFMs) relating fault detection residual to the disturbances and faults is quantified by the proposed v$$ v $$‐gap metric, which allows a more differentiated treatment of the disturbance and fault signals for better fault detection. Then, the optimal design of the ADD‐GPIO is modeled as a mixed‐integer optimization problem and the Lagrangian relaxation is adopted to find the near‐optimal parameters of the ADD‐GPIO at less computation costs in real application. Finally, both simulation and lab experiments of a two‐wheeled self‐balancing robot are carried out to validate the improved performance of the proposed method.

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Set‐membership state estimation for delayed switched systems with interval uncertainty: A zonotopic approach

Zhou,

Chen,

et al. 2024

Adaptive Control & Signal

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SummaryConsidering the existence of time delay and interval uncertainty, a set‐membership state estimation problem based on zonotopes is studied for discrete‐time switched systems subject to unknown but bounded noises. To conform the actual situation, a state observer asynchronous with the subsystem is designed. The performance is introduced to attenuate the effect of noises in the observer design. By dealing with the interval uncertainty, the design approach of observer is given and transformed into a convex optimization problem which can be solved off‐line. The zonotope and the estimation interval containing state are provided based on the observer. Finally, a numerical example is given to verify the effectiveness of the proposed algorithm.

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Optimal Zonotopic Kalman Filter-based State Estimation and Fault-diagnosis Algorithm for Linear Discrete-time System with Time Delay

Cited by 5 publications

References 39 publications

Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review

Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review

A generalized proportional‐integral observer for fault detection: An approximate input disturbance decoupling approach

Set‐membership state estimation for delayed switched systems with interval uncertainty: A zonotopic approach

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