Fault detection for wireless networked systems with compressed measurements

Neural net have proven to be capable of solving the motor monitoring and fault detection problem using an inexpensive, reliable and noninvasive procedure. The neural net, unfortunately, cannot provide heuristic knowledge about the motor or the fault detection process. This paper introduces a novel hybrid fuzzy/neural fault detector that uses the learning capabilities of the neural net to detect if a motor has an incipient fault. Once the fuzzy/neural fault detector is trained, heuristic knowledge about the motor and the fault detection process can also be extracted. With better understanding of the heuristics through the use of fuzzy rules and fuzzy membership functions, we can have a better understanding of the fault detection process of the system; thus we can design better motor protection systems. The electric motors in industry are exposed to a wide variety of environments and conditions. These factors, coupled with the natural aging process of any machine, make the motor subject to incipient faults. These incipient faults, left undetected, contribute to the degradation and eventual failure of the motors. With proper monitoring and fault detection schemes, the incipient faults can be detected; thus maintenance and down-time expenses can be reduced while also improving safety. In this paper, motor bearing faults in single-phase induction motors are used to illustrate this novel system. This illustration demonstrates the successful training of a hybrid fuzzy/neural system that can provide accurate fault detection, and gives the heuristic reasoning for the fault detection procedure.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

UKF-based fault detection for nonlinear stochastic systems with strong noise and communication protocols.

This paper proposes a fault detection method based on an extended state observer (ESO). This method of fault detection is achieved in two-stage process, residual generation and fault prediction. Residual generation uses an ESO to estimate the state variables of the three-tank system. Fault prediction uses an ellipsoid threshold method to predict the faults. Twelve kinds of stepwise faults are detected successfully in the three-tank system. The experimental results elaborate the effectiveness of the online fault detection based on ESO.

In this paper, we consider the problem of fault detection (FD) and isolation in the aeration system of an activated sludge process. For this study, the dissolved oxygen in each aerated zone is assumed to be controlled automatically. As the basis for an FD method we use the ratio of air flow rates into different zones. The method is evaluated in two scenarios: using the Benchmark Simulation Model no. 1 (BSM1) by Monte Carlo simulations and using data from a wastewater treatment plant. The FD method shows good results for a correct and early FD and isolation.

Fault detection in continuous industrial processes is crucial to ensure production, quality, safety, environmental and economic aspects. Reliable fault detection systems have become even more necessary due to increasingly stringent regulations. The complexity of processes, on the one hand, and the availability of data, on the other, have spread the adoption of the data-driven approach for industrial fault detection. Deep Reinforcement Learning (RL) has recently been applied for this purpose, mainly in rotating machinery and rolling bearings. In chemical processes, most RL applications are in process control. The main contribution of this work lies in proposing a reinforcement learning (RL)-based framework for fault detection in continuous chemical processes. The challenges are related to the large number of variables, nonlinear relationships and changing operational dynamics, to name a few. The classic Tennessee Eastman Process (TEP) benchmark was used as a case study. The results were compared with Principal Component Analysis (PCA), commonly used for chemical process monitoring, dynamic PCA (DPCA), and neural networks. Compared to PCA, the RL approach showed considerably lower missed detection rate (MDR) and time to detection (TTD) while maintaining reasonable false alarm rate (FAR) for all hard-to-detect faults, contributing to early fault detectability. Similar results were obtained with regard to DPCA. Compared to the supervised approach, RL proved to be more flexible regarding model parameterization, which can be very useful for practical aspects of industrial fault detection. The results show that RL-based approaches can be very useful for fault detection in continuous, non-linear, and time-varying systems, such as chemical processes. For example, with regard to adaptive alarm thresholds and the detection of known and unknown faults. The latter represents a major challenge in industrial practice.

This paper deals with the fault detection, diagnosis and control problems of time delay systems by designing a disturbance observer (DOB). In proposed method, the DOB estimates the changes occurred as an input disturbance. In fact, the DOB is implemented as a residual generator for the system. Subsequently, the estimated changes can be used to compensate the effect of faults and plant alterations directly. In this method, the estimated disturbances can be used for the FDI purpose and the final system would be fault tolerant, without the need to have all other faults isolable and identifiable. It provides a simple, low cost and effective fault detection and isolation method which can be applied to more practical systems. The applicability and effectiveness of the proposed method is illustrated by the simulations provided for an industrial water pressure control system which is supposed to be subject to faults.

The fault detection problem is investigated for discrete-time linear uncertain systems. Instead of designing a fault detection system from the viewpoint of observer design for robust residual generation, an adaptive threshold approach is proposed to attain robustness against disturbance and norm-bounded model uncertainty. The main goal of the research is to develop a threshold design method that could establish an appropriate trade-off between false alarms and missed fault detection in the presence of model uncertainty. For this purpose, the H∞ optimization technique is adopted in the linear matrix inequality framework to compute the unknown parameters of an adaptive threshold. It is shown that the proposed fault detection system based on an adaptive threshold depends only on the system parameters and the control input of the monitored system. It is independent of robust residual generator designs in traditional observer-based fault detection systems. The effectiveness of the proposed approach is verified on two well-known benchmark systems: a direct-current motor and three tank systems. Several types of faults are successfully detected in both applications.

The self-attention mechanism has advantages in analyzing the internal characteristics of data and capturing local information. Therefore, it is applied to fault detection. Since complex industrial processes usually contain a mixture of properties, such as nonlinearity, dynamics, and non-Gaussianity, this places higher demands on fault detection methods. To further improve the performance of the self-attention mechanism for fault detection in complex industrial processes, an innovative self-attention mechanism integrating the adaptive double subspace (ISA-ADS) method is proposed for fault detection. First, the query matrix, the key matrix, and the value matrix of the self-attention mechanism are designed so that they can better focus on local information through the adaptive sample weight allocation strategy. At the same time, this design reduces the interference of nonlinear and noisy information and amplifies the impact of important fault information in the sample. Second, considering the problem of Gaussian and non-Gaussian distributions of the data and the different degrees of influence of different subspaces on the final process monitoring results, an adaptive Gaussian and non-Gaussian double subspace fault detection model based on the self-attention output matrix is established, which adaptively assigns different weights to different subspaces by determining their importance. The statistics of different subspaces are fused using the Bayesian inference and a new adaptive monitoring statistic is constructed to obtain a more accurate monitoring of the process state. Finally, the ISA-ADS is validated using the Tennessee Eastman (TE) process and the actual polyethylene production process. The results show that ISA-ADS has a low false alarm rate and a high fault detection rate, verifying the effectiveness of its fault detection performance.

This paper considers the problem of fault detection and identification (FDI) in applications carried out by a group of unmanned aerial vehicles (UAVs) with visual cameras. In many cases, the UAVs have cameras mounted onboard for other applications, and these cameras can be used as bearing-only sensors to estimate the relative orientation of another UAV. The idea is to exploit the redundant information provided by these sensors onboard each of the UAVs to increase safety and reliability, detecting faults on UAV internal sensors that cannot be detected by the UAVs themselves. Fault detection is based on the generation of residuals which compare the expected position of a UAV, considered as target, with the measurements taken by one or more UAVs acting as observers that are tracking the target UAV with their cameras. Depending on the available number of observers and the way they are used, a set of strategies and policies for fault detection are defined. When the target UAV is being visually tracked by two or more observers, it is possible to obtain an estimation of its 3D position that could replace damaged sensors. Accuracy and reliability of this vision-based cooperative virtual sensor (CVS) have been evaluated experimentally in a multivehicle indoor testbed with quadrotors, injecting faults on data to validate the proposed fault detection methods.

In response to the challenges posed by the dynamic and multi‐stage characteristics of complex multi‐batch processes in process monitoring, the Baseline Standardization (BS) strategy is proposed, which, together with Multiway Principal Component Analysis (MPCA), forms the BS‐MPCA fault detection method. BS constructs a baseline batch that changes over time based on the mean of samples at the same time point from different batches. Due to inevitable fluctuations in the baseline batch, a Gaussian filter is applied to smooth the baseline batch in order to enhance its robustness. The dataset is standardized based on the standard deviation of samples at the same time point in different batches and the baseline batch, resulting in the standardized dataset. BS scales the data to a unified scale, which eliminates the dynamic and multi‐stage characteristics of the process. The resulting standardized dataset meets the assumption of independent and identically distributed statistics and . Based on this, the MPCA method is used for feature extraction and fault detection. BS‐MPCA is applied to fault detection in numerical processes and the Three‐tank process. Experimental results show that compared to MPCA, DPCA, MDPCA, VBSPCA, KNN, and WKNN methods, the BS‐MPCA fault detection method achieves a higher fault detection rate. Additionally, it eliminates the dynamic and multi‐stage characteristics of the process during the modeling phase, and its detection model outperforms the models of the comparison methods. Theoretical analysis and simulation experiments show that the BS‐MPCA method is suitable for fault detection in multi‐batch processes, whether they have simultaneous or individual dynamic and multi‐stage characteristics. It more effectively ensures the safety of the production process and the high quality of the products.

In this paper, the problem of fault detection and<br> isolation in the attitude control subsystem of spacecraft formation<br> flying is considered. In order to design the fault detection method, an<br> extended Kalman filter is utilized which is a nonlinear stochastic state<br> estimation method. Three fault detection architectures, namely,<br> centralized, decentralized, and semi-decentralized are designed based<br> on the extended Kalman filters. Moreover, the residual generation<br> and threshold selection techniques are proposed for these<br> architectures.

Fault Detection and Diagnosis for Nonlinear Systems: A Support Vector Machine Approach

A Data-Driven Fault Detection and Diagnosis by NSGAII-t-SNE and Clustering Methods in the Chemical Process Industry

Abstract. It is a great challenge to apply a diagnostic system for sensor fault detection to engine test beds. The main problem is that such test beds involve frequent configuration changes or a change in the entire test engine. Therefore, the diagnostic system must be highly adaptable to different types of test engines. This paper presents a diagnostic method consisting of the following steps: residual generation, fault detection and fault isolation. As adaptability can be achieved with residual generation, the focus is on this step. The modular toolbox-based approach combines physics-based and data-driven modeling concepts and, thus, enables highly flexible application to various types of engine test beds. Adaptability and fault detection quality are validated using measurement data from a single-cylinder research engine and a multicylinder diesel engine.

A fault detection technique for air-source heat pump water chiller/heaters