Image Based Fault Detection Algorithm for Flexible Industrial Assembly Line

Fault detection (FD) algorithms based on supervisory control and data acquisition (SCADA) data have been widely used in the operation and maintenance (O&M) of wind turbines (WTs). However, the performance of FD models will degrade due to the time-varying operating conditions (TVOC). Incremental learning (IL) methods can be employed to update the models online to adapt to TVOC, but the error accumulation caused by false negatives will lead to a continuous decrease in the FD rate (FDR). A novel IL method considering false negatives is proposed to improve the performance of WT FD models. First, a data buffer is built to cache some new normal data used in model updating. Second, a processing strategy for false negatives is proposed to block some high-risk data from being added to the buffer, thereby weakening the error accumulation. Third, when the buffer is full, update the FD model using the data in it. A real-world SCADA dataset with gearbox faults and four different FD algorithms with various model updating strategies are used in the experiments. The results demonstrate that the proposed method can lower the false alarm rate (FAR) of all four FD algorithms. After processing false negatives, for example, for the FD model based on Gaussian kernel regression, its FDR increased by about 11%, and for the FD model based on multivariate state estimation technique, its FDR increased by about 14%. The results of hyperparameter experiments and early stopping experiments show that the proposed method has good potential for practical applications.

Flexible Assembly Systems - Hardware Components and Features: Basic components of a FAS.- Classification of flexible assembly systems.- Examples of industrial installations.- Issues in Design, Planning and Scheduling of Flexible Assembly: FAS design issues.- Network design for material flow systems.- Design for assembly.- Assembly planning.- Planning and scheduling.- Loading and Routing Decisions in Flexible Assembly Systems: Description of a flexible assembly system.- Optimization of station workloads and product movements.- Design and balancing of flexible assembly lines.- Numerical examples.- Simultaneous loading and routing.- Sequential loading and routing.- Loading and Routing Decisions with Assembly Plan Selection: Simultaneous loading, routing and assembly plan selection in a general flexible assembly system.- Simultaneous loading, routing and assembly plan selection in a flexible assembly line.- Loading and Scheduling in Flexible Assembly Cells: Loading and scheduling in a robot assembly cell.- Loading and sequencing in printed circuit board assembly.- Production Scheduling in Flexible Assembly Lines: Flexible assembly line with limited intermediate buffers.- Flexible assembly line with no in-process buffers.- Global lower bounds for flexible flow lines with unlimited buffers.- Just-in-time scheduling of flexible assembly lines.- Multilevel scheduling of flexible assembly lines with limited intermediate buffers.- Machine and Vehicle Scheduling in Flexible Assembly Systems: Dispatching scheduling.- Machine and vehicle scheduling - a multi-level approach.- Machine and vehicle scheduling - a single-level approach.

The real-time fault detection and diagnosis algorithm of a liquid rocket engine is the basis of online reconfiguration of guidance and the control system of a launch vehicle, which is directly related to the success or failure of space mission. Based on previous related works, this paper carries out comparative experimental studies of relevant intelligent algorithm models for real-time fault detection engineering application requirements of a liquid hydrogen–oxygen rocket engine. Firstly, the working state and detection parameters’ selection of a hydrogen–oxygen engine are analyzed, and the proposed three real-time intelligent fault detection algorithm model design methods are elaborated again. Fault detection calculation and analysis are carried out through normal test data and fault test data. The comparative analysis results of real-time intelligent fault detection algorithm models is presented from three dimensions: detection time, fault detection, and stability and consistency. Finally, based on a correlation analysis, a comprehensive intelligent fault diagnosis model design framework is given to further solve the requirements of real-time fault detection and diagnosis engineering development of a liquid rocket engine, a complex piece of equipment.

Fault detection and diagnosis algorithms for an open-cycle liquid propellant rocket engine using the Kalman filter and fault factor methods

This paper presents a sliding mode-based adaptive fault detection and emergency control algorithm for implementation in fail-safe systems of autonomous vehicles. The overall algorithm is comprised of a fault detection part and a fail-safe control part. For the former, sliding mode observer-based fault detection algorithms were developed for environment and chassis sensors, including LiDAR, Radar, and acceleration sensors. Unidentified fault signals from the sensors are reconstructed through the adaptive sliding mode observer. The reconstruction is based on the MIT rule through the use of an estimated sensitivity parameter. For the latter, a sliding mode control (SMC)-based emergency control method designed to respond to fault occurrences has been proposed to ensure the functional safety of autonomous vehicles. An adaptive gain parameter was designed, taking convergence time into consideration, to secure consistent and rapid responses from the controller. When the detection algorithm detects a fault, the appropriate control input is computed by a lower controller for the vehicle. This control input is calculated based on the last scene information obtained from an upper controller. The performance of the proposed fault detection and control algorithms has been evaluated through simulations and actual vehicle tests of various scenarios.

Fault localization has become an increasingly challenging issue in high-performance computing (HPC) systems. Various techniques have been used for HPC systems. However, as the HPC systems scale out, resulting in the rapid deterioration of the existing techniques. In this context, we propose a message-passing based fault localization framework, namely MPFL, which provides a light-weight distributed service using tree-based fault detection (TFD) and fault analysis (TFA) algorithms. In essence, MPFL serves as a fault localization engine within message-passing libraries by enabling several system middleware such as job scheduler to provide abnormal information. We present details of the MPFL framework, including the implementation of TFD and TFA. Further, we develop the fault localization engine prototype within MVAPICH2. The experimental evaluation is performed on a typical HPC cluster with 10 computing nodes, which demonstrate the capability of MPFL and show that the MPFL service does not affect the performance of an application in practice.

This thesis presents a critical appraisal of several differing approaches to the design and testing of fault detection (FD) algorithms monitoring the instrumentation used in the alternated aerobic/anoxic cycles (AC) process for nitrogen removal. Several features are selected as the basis of the FD, involving the slope and the timing of the process measurements of the various nitrogen compounds. Two separate FD algorithms have been developed for the anoxic and the aerobic phases, requiring a separate tuning but sharing the same principles: first some low-level checks are performed on the raw signals, discriminating gross malfunctions like missing data and spikes, then more sophisticated methods are used to investigate the presence of more subtle anomalies that were not detected by the previous screening. The FD problem is treated either in terms of classification problem, testing different algorithms such as binary trees, support vector machines (SVM) and principal component analysis (PCA), or as a forecasting one, using the Bayesian theory to predict the faulty or normal state of the process based on the previous records. An operational data set obtained from a municipal plant was used to first train the algorithm. However, due to the fairly limited information which could be extracted, a more comprehensive data set was created building an AC model based on the standard Benchmark simulation model with improved nitrogen kinetics and seasonal temperature variations. Detailed sensor models were also included, so that the occurrence of faults could be totally controlled, both in kind and timing. The performances of the various methods on either the operational and the synthetic datasets have been assessed comparing the anomalies detected by the methods with those actually observed. While the great majority of the gross faults is successfully detected by the preliminary screening, differing performances of the subsequent finer detection are obtained, depending on both the quality of data set and the detection method used: poorer results are observed using the plant data, in part due to an insufficient characterization of the fault events and in part due to the limited number of signals monitored. The higher availability of measurements provided by the numerical model, instead, enhances the discrimination capabilities of the tested methods, especially the nonlinear SVM, while the PCA-based approach and the Bayesian predictor results less affected by a change in the combination of diagnostic parameters used.

The major concern in a Controller Area Network based safety critical system are node failures, its early detection and failure tolerance. Aiming at the improvement of node fault detection timing response and reliability of a CAN based distributed system, this paper mainly focuses on a new fault detection and tolerance algorithm named Distributed Node Fault Detection and Tolerance [DNFDT] Algorithm for Controller Area Network. The purpose of the algorithm is to have an effective fault detection method for (n-1) node faults for n node system by reducing the fault detection cycle timings and by limited checking on fault free nodes. A four node experimental hardware platform was implemented and performance evaluation of the time elapsed for one complete fault detection cycle under varying CAN bus loads have been obtained.

A fault-tolerant technique of a grid-side converter (GSC) is a very important task because the unbalanced grid power endangers the overall system. Since the GSC is very sensitive to grid disturbance, the complete system needs to be stopped suddenly once an open-switch fault occurs. To improve the reliability of system, the continuous operation should be guaranteed. In this paper, a redundant topology based fault-tolerant algorithm is proposed for a GSC in a wind power generation system. The proposed scheme consists of the fault detection and fault-tolerant algorithms. The fault detection algorithm employs the durations of positive and negaitive cycles of three-phase grid currents as well as normalized root mean square (RMS) currents. Once a fault is detected, the corresponding faulty phase is identified and isolated to enable the fault-tolerant operation. The faulty phase is replaced by redundant one rapidly to recover the original shape of the grid currents, which ensures the continuity in operation. In contrast with the conventional methods, the proposed fault detection and fault-tolerant algorithms work effectively even in the presence of the open faults in multiple switches in the GSC. Simulation results verify the effectiveness of the proposed fault diagnosis and fault-tolerant control algorithms.

substantially from the prescribed or expected characteristics. To accommodate failures, the baseline inner-loop control is assisted by a fault detection and isolation algorithm tasked with monitoring and reporting control surface health. Similarly, the baseline outer-loop guidance is augmented with an adaptive neural network forming an adaptive outer-loop guidance system. These adaptive subsystems have been formulated for, and applied to, a transport aircraft model in a nonlinear, high-fidelity aircraft simulation environment to demonstrate the concept. Included results demonstrate that the fault detection and isolation and adaptive outer-loop functionality consistently improve tracking performance during both piloted maneuvers and autonomous waypoint path following. In some cases, these retrofit subsystems enable tasks to be completed that were not previously feasible. Adaptive enhancement of the robustness of existing non-adaptive systems has been an area of significant interest for many years. Although application in the aircraft community is of great benefit for maximizing performance and safety for unexpected changes in the dynamics due to flight control failures, damage, and adverse environmental conditions, certification of these technologies for safety-critical flight systems is challenging. The approach described here is developed under the constraint of maintaining a retrofit architecture thereby allowing the baseline, previously certified, software to remain. The augmented modules serve to assist systems already in place. This paper discusses the development and application of independent and modular adaptive subsystems that are retrofitted to existing guidance and control architectures. The baseline inner-loop control is assisted by a fault detection and isolation (FDI) algorithm tasked with monitoring and reporting control surface health. Similarly, the baseline outer-loop (BOL) guidance is augmented with an adaptive neural network collectively forming an adaptive outer-loop (AOL) guidance system. In general, an adequate inner-loop feedback control law is necessary to faithfully track guidance commands, and various inner-loop control design techniques can provide the requisite performance. Even in the case of degraded inner-loop tracking capability, the outer-loop guidance system may still meet its objectives by inflating inner-loop commands to compensate for inner-loop tracking errors. Nonetheless, faithful innerloop tracking is desirable to reduce the burden on other subsystems, and this is especially the case for piloted

Transmission lines are protected by distance relays as main protection and overcurrent relays as backup protection. Both of distance relays and overcurrent relays have mal-operation during high Impedance faults, faults in the week in-feed systems and faults occurred during power swing. Fluctuations in voltage and current during power swing cause fluctuations in measured impedance by distance relay. In some measured impedance moves to protected zone and causes mal-operation. So, distance relay is blocked duration of a power swing. In cases that fault occurs during a power swing, the relay must detect fault inception. In this paper, a new algorithm for fault detection in cases with low fault current and fault detection during power swing is presented. Diagnostic criteria in this algorithm are the calculation of the spectral energy of current signal by the Adaptive Short-Time Fourier transform (ASTFT). Fault currents consist of a considerable amount of high-frequency components which they do not exist the in-line current during overload or power swing. The presented method is based on cooperation between existing fault detection methods and a fault detection using ASTST. Different conditions are Simulated in this paper in the software / EMTDC PSCAD and MATLAB to verify of correct performance of the proposed algorithm.

This paper proposes a new method for estimating the topology of radial distribution networks and a novel data-driven fault detection technique using smart meter data. An improved method to estimate voltage sensitivity coefficients from smart meter data is proposed that takes into account the variability of the transformer voltage. These coefficients are used in both the topology estimation and the fault detection algorithms. In the topology estimation algorithm, an improved graph learning algorithm, which iteratively constructs the network graph, is devised. In the fault detection algorithm, the objective is to look for sudden changes in the estimated voltage sensitivity coefficients. The performance of the proposed methods is first assessed on randomly generated topologies under various noise conditions. Then, the performance of the algorithms is validated using real smart meter measurements obtained from Australian distribution networks. The results show a significant improvement in the accuracy of estimated topologies compared to the state-of-the-art methods while the fault detection technique is successful in detecting faults using real smart meter data.

In data-based method for fault detection, principal component analysis (PCA) has been used successfully for fault detection in system with highly correlated variables. The aim of this paper is to combine the exponentially weighted moving average (EWMA) control scheme with PCA model in order to improve fault detection performance. In fact, PCA is used to provide a modeling framework for the develop fault detection algorithm. Because of the ability of EWMA control scheme for detecting small changes, this technique is appropriate to improve the detection of a small fault in PCA model. The performance of the PCA-based EWMA fault detection algorithm is illustrated and compared to conventional fault detection methods using simulated continuously stirred tank reactor (CSTR) data. The results show the effectiveness of the developed algorithm.

In this paper, the problem of accelerometer fault detection is studied for the rotary steerable drilling tool system (RSDTS) which is modeled by a nonlinear stochastic system. Owing to the strong vibration in the drilling process, RSDTSs are inevitably influenced by strong measurement noise, which brings in substantial difficulties in the fault detection. Firstly, a modified particle filtering algorithm is proposed to improve the accuracy of state estimation. Next, by means of the moving average method, the residual which approximatively obeys the χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> distribution is designed when the estimation error is small enough. According to the property of the χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> distribution, the window length of the moving average method is determined. Moreover, a fault detection algorithm is presented, and false alarm rate and missed detection rate are quantitatively analyzed for nonlinear stochastic systems. Finally, the effectiveness of the developed fault detection method is verified by simulation and experiment on the prototype of the RSDTS.

Crack is a common fault of rotor systems. The research on crack fault detection methods is mainly divided into numerical and experimental studies. In numerical research, the current fault detection algorithms based on deep learning are mostly applied to bearings and gearboxes, and there are few studies on rotor fault diagnosis. In experimental research, the rotors used in an experiment are mostly single-span rotors. However, there are complex structures such as multi-span rotor systems in the actual industrial field. Thus, the fault detection algorithms that have been successfully applied on single-span rotors have not been verified on complex rotor systems. To obtain a fault signal close to the actual asymmetric shaft system of an asymmetric rotor system and validate the fault detection method, the crack fault detection platform is designed and built independently. We measure the vibration signals of three channels under five working conditions and establish an intelligent detection method for crack location based on a residual network. The factors that influence fault detection performance are analyzed, and the influence laws are discussed. Results show that the accuracy and anti-noise performance of the proposed method are higher than those of the commonly used machine learning. The average accuracy is 100% when SNR (signal-to-noise ratio) is greater than or equal to −2 dB, and the average accuracy is 98.2% when SNR is −4 dB.