No abstract

Due to the presence of actuator disturbances and sensor noise, increased false alarm rate and decreased fault detection rate in fault diagnosis systems have become major concerns. Various performance indexes are proposed to deal with such problems with certain limitations. This paper proposes a robust performance-index based fault diagnosis methodology using input–output data. That data is used to construct robust parity space using the subspace identification method and proposed performance index. Generated residual shows enhanced sensitivity towards faults and robustness against unknown disturbances simultaneously. The threshold for residual is designed using the Gaussian likelihood ratio, and the wavelet transformation is used for post-processing. The proposed performance index is further used to develop a fault isolation procedure. To specify the location of the fault, a modified fault isolation scheme based on perfect unknown input decoupling is proposed that makes actuator and sensor residuals robust against disturbances and noise. The proposed detection and isolation scheme is implemented on the induction motor in the experimental setup. The results have shown the percentage fault detection of 98.88%, which is superior among recent research.

This paper discusses a framework for fault detection in nonlinear processes. A novel subspace aided parity-based data-driven technique is proposed using the so-called just-in-time learning (JITL) approach. The idea of the proposed technique is to choose a set of the most similar data samples from the database for each test data sample using the JITL approach to address the nonlinearity problem. The parity vector is constructed for each test sample using the selected data samples to generate the residual signal. The salient features of the proposed technique include easy and simple implementation together with effectiveness in fault detection in the presence of disturbances and measurement noise. The fault detection scheme is so designed that it exhibits robustness against sensor noise and disturbances and sensitivity to faults. A case study for the fault detection of a nuclear research reactor (NRR) is presented to demonstrate the efficacy of the technique. The NRR is a highly nonlinear and complex process. Two faults of NRR, namely external reactivity insertion and control rod withdrawal, are successfully detected using the proposed approach.

We consider the problem of fault detection and isolation for the penicillin fermentation process. A penicillin fermentation process is a highly complex and nonlinear dynamic process with batch processing. A data-driven approach is utilized for fault diagnostics due to the availability of huge batch processing data and the unavailability of an analytical model. To address the non-linearity, a subspaceaided parity-based residual generation technique is proposed by using a just-in-time learning approach. For the just-in-time learning approach, the most similar data samples are selected from the database for incoming test samples and a subspace aided parity-based residual is generated using these samples. The designed fault detection technique is implemented for the penicillin fermentation process to demonstrate real-time health monitoring of the process under sensor noise and process disturbances. Two sensor faults and an actuator fault are considered and successfully detected using the proposed technique. Further, a fault isolation approach is developed to isolate these faults and their location has been identified. A case study is given to show the improvement offered by the proposed technique for the fault detection rate and minimization of the false alarm rate as compared to the existing techniques for the penicillin fermentation process.INDEX TERMS Data-driven, fault diagnostics, process monitoring, subspace identification, just-in-time learning, penicillin fermentation processThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

This paper presents two new fault models for networked systems. These fault models are more realistic and generalized for networked systems in the sense that they can represent the effects of fault at the node and network levels. At the network layer, the uncertain effects of the network lines are modeled using a Markov chain with complex transition probabilities simultaneously with the stochastic behavior of the network using a Bernoulli process. A new output feedback-based controller, which is two-mode dependent and considers network uncertainties and output measurements for gain calculation, is presented. Using the tools of robust control and stochastic stability, linear matrix inequality-based sufficient conditions are derived. The proposed controller successfully maintains the system’s performance by tolerating the effects of simultaneous sensor and actuator faults, ensuring the stability of networked loops. Simulation results verify the applicability of the presented fault-tolerant control against multiple simultaneous faults.