Developing an Approach for Fault Detection and Diagnosis of Angular Velocity Sensors

This paper presents a fault detection and diagnosis approach based on Mahalanobis distance (MD) for monitoring continuous processes. The Mahalanobis distance is first used to detect sensor faults, and its control limit is determined by the empirical method via specification a significance level. The main idea behind Mahalanobis distance is using the whole data information instead of dimensionality techniques such as partial least squares, canonical variate analysis, and principal component analysis. The data reflected by the detection index, MD, is analysed by a fault diagnosis method, i.e, contributions plots. An integrated approach for fault detection and diagnosis is presented and tested by two simulation studies, Monte Carlo simulation data and continuous stirred tank reactor chemical process(CSTR). The results illustrate the reliability of the proposed approach in detecting and diagnosing sensor faults.

Structural health monitoring (SHM) is a non-destructive evaluation technique that utilizes sensor data for assessing the condition of civil infrastructure. Sensors in SHM systems may experience faults, which may influence the accuracy, reliability, and performance of SHM systems. The timely detection, isolation, and accommodation of sensor faults in SHM systems has been the focus of sensor fault diagnosis (FD) approaches, which have increasingly been employing artificial intelligence (AI) algorithms due to the effectiveness of AI in sensor FD. However, current AI-based FD approaches require transmitting large amounts of raw sensor data to centralized servers for offline analysis, resulting in inefficiencies as well as computational burdens on centralized servers. This paper introduces a decentralized sensor fault diagnosis (DSFD) approach for wireless SHM systems using Artificial Intelligence of Things (AIoT). In particular, AI-based FD models are embedded into wireless sensor nodes of SHM systems to detect, isolate, and accommodate sensor faults. By embedding the FD models into the wireless sensor nodes, only high-level information, specifically the status of the sensors, is transmitted to centralized servers. As a result, data transmission inefficiencies as well as computational burdens on centralized servers are reduced. The proposed DSFD approach is validated in a controlled laboratory experiment, in which custom- built wireless senor nodes are installed on a test structure that is dynamically excited using a shake table. After training and embedding the AI-based FD models into the custom-built wireless sensor nodes, sensor faults are artificially injected into the sensor data, demonstrating the ability of the DSFD approach to diagnose sensor faults in a decentralized manner. The results of the validation test corroborate the capability of the proposed approach to efficiently ensure the accuracy, reliability, and performance of SHM systems.

Fault detection and diagnosis in process data using one-class support vector machines

In a chiller plant, primary or critical sensors are used to control the system operation while secondary sensors are installed to monitor the performance/health of individual equipment. Current sensor fault detection and diagnosis (SFDD) approaches are not applicable to secondary sensors which usually are not involved in the system control. Consequently, a hybrid multiple sensor fault detection, diagnosis and reconstruction (HMSFDDR) algorithm for chiller plants was developed. Machine learning and pattern recognition were used to predict the primary sensor faults through the comparison of the weekly performance curves. With the primary sensor signals reconstructed, the secondary sensor faults were estimated based on mass and energy balance. By applying the algorithm with various logged plant data and comparison with site checking results, a maximum of 75% effectiveness could be achieved. The merits of the present approach were further justified through off-site sensor testing which reinforced the usefulness of proposed HMSFDDR algorithm.

A deep learning unsupervised approach for fault diagnosis of household appliances

Fault detection isolation and diagnosis of multi-axle speed sensors for high-speed trains

In order to meet the requirements of multi-parameter and real-time fault diagnosis of China VI vehicle emission standards diesel engine air system, this paper focuses on the research of the diesel engine air system fault detection and diagnosis method based on deep learning to improve the operational safety and fault diagnosis efficiency. In this paper, the air system fault detection and diagnosis are completed based on the AE model and the CNN model, combined with the actual operation of the diesel engine. Among them, the AE model successfully detected all real-time operation faults, and the false detection rate on the health data was 0.1162%; the CNN model obtained a 90.77% fault diagnosis accuracy rate on the test set of the real-time operation data set. The results show that the model has high accuracy in the diagnosis of diesel engine faults, which is of great significance to the application of deep learning based on big data processing.

Fault detection, diagnosis and calibration of heating, ventilation and air conditioning sensors by combining principal component analysis and improved bayesian inference

In this paper, a high-gain nonlinear observer based fault detection and diagnosis (FDD) approach is proposed for a general class of nonlinear uncertain systems with measured output probability density functions (PDFs). The objective of the presented FDD algorithm is to use the measured output probability density functions (PDFs) and the input of the system to construct a exponential observer-based residual generator such that the fault can be detected and diagnosed. The main result is given in a constructive manner by developing a novel nonlinear observer, without resort to any linearization. By a coordinates transformation, the design of the proposed observer does not necessitate the resolution of kind of linear matrix inequalities (LMIs) and its expression is explicitly given. The exponential convergence of the errors in the presence of parameters uncertainties is proved to guarantee the fastness of the proposed fault diagnosis scheme. Furthermore, the bound of the estimation errors in the presence the faults is minimized by appropriately choosing the parameters of the presented observer. Finally a simulation example is given to illustrate the efficiency of the proposed fault detection and diagnosis method.

To online monitor process, a combined approach of fault detection and diagnosis based on Lifting Wavelets and Moving Window PCA (LW-MWPCA) was presented. Firstly the data were pre-processed to remove noise and spikes through lifting scheme wavelets, and then MWPCA was used to diagnose faults. To validate the performance and effectiveness of the proposed scheme, LW-MWPCA was applied to diagnose the faults in TE Process. The results were given to show the effectiveness of these improvements for fault diagnosis performance in terms of low computational cost and high fault diagnosis rate.

The speed and localization information of the high-speed trains are essential and significant for train control systems. The occurrence of faults in SDUs (Speed Distance Units) will affect the accuracy and effectiveness of location reports to ensure the operation safety of high-speed trains. The fault detection and diagnosis method for SDUs are focused in this paper. By using the on-board GPS (Global Positioning System) measurements, we propose a parallel-filtering-based architecture for detecting the faults in odometers that use the pulse-counting information to estimate the speed and travelling distance of the trains. According to the characteristics of different fault modes, including the hard fault, soft fault and implicated fault, a rule-based fault diagnosis approach is proposed to identify the detected fault and take suggested maintenance measures, where a conservative determination strategy for utilizing the results of the two filters is demonstrated to decrease the missed detection probability. The field operation data from the high-speed train is employed to build the simulation scenarios. The simulation results demonstrate the performance of the proposed method and illustrate its potentials in real-time fault diagnosis and maintenance for speed distance units.

Wireless Body Area Networks (WBAN) is the sensor network used for monitoring health information in e-health systems. WBAN is a combination of sensors used to obtain vital information from the body. It is a special type of WSN. WBAN technology should handle the data in a smart way by reacting to the monitored data and to evaluate the data. It requires fault detection and diagnosis methods for sensors used in WBAN. This paper gives a survey of different types of neural-network approaches for faults detection and diagnosis in WBAN using neural network.

The present paper proposes a novel data-driven Fault Detection and Diagnosis algorithm for induction motors based on Motor Current Signature Analysis. Principal Component Analysis is used to reduce the three-phase currents space in two dimensions. Then, Kernel Density Estimation is adopted to estimate the Probability Density Function of healthy and of each faulty motors, which will give typical patterns that can be used to identify each fault. Kullback-Leibler divergence is used as an index to identify the dissimilarity between two determined probability distributions, that allows the automatic identification of distinct fault types. Several simulations and experimental results are carried out using two benchmarks in order to verify the effectiveness of the proposed methodology: the first is used to prove appropriateness of the method for air gap eccentricity fault diagnosis and the second is used to prove suitability of the method for rotor broken bars and connectors fault diagnosis. Simulations and classification results prove that the proposed Fault Detection and Diagnosis procedure is able to detect and diagnose different induction motor fault types.

A statistical training data cleaning strategy for the PCA-based chiller sensor fault detection, diagnosis and data reconstruction method

This paper suggests an approach for fault detection and diagnosis capable to detect new operation modes online. The approach relies upon an evolving fuzzy classifier able to incorporate new operational information using an incremental unsupervised clustering procedure. The efficiency of the approach is verified in fault detection and diagnosis of an induction machine. Experimental results suggest that the approach is a promising alternative for fault diagnosis of dynamic systems when there is no a priori information about all failure modes. It is also attractive for incremental learning of diagnosis systems with streams of data.KeywordsEvolving Fuzzy SystemsParticipatory LearningAdaptive Fault Detection and Diagnosis