Multiple Fault Conditions Research Articles

Event-Based Sequential Prognosis for Uncertain Hybrid Systems With Intermittent Faults

This paper addresses the prognosis problem for hybrid systems with intermittent faults and uncertain parameters. First, a diagnostic hybrid bond graph in linear fractional form is used to model the uncertain hybrid system to generate the mode-dependent adaptive thresholds for fault detection purpose. Then, a global combinative fault signature matrix integrating independent and dependent augmented global analytical redundancy relations is proposed to improve the system fault isolability under the multiple-fault condition. After the possible fault set is isolated, an adaptive reinforcement unscented Kalman filter is introduced to identify the intermittent fault magnitude, where an auxiliary indicator is used to capture the fault appearing and disappearing time steps. To describe the degradation trend of the intermittent fault, a dynamic model with a mode-dependent degradation coefficient is used. Taking the variation of the degradation coefficient into account, an event-based sequential prognosis method is proposed, where the prognoser is only reactivated if the discrete event representing mode change is observed and its associated conditions are satisfied. Finally, the key concept of the proposed method is verified by experimental studies.

Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming

Dissolved gas analysis (DGA) of transformer oil is considered to be the utmost reliable condition monitoring technique currently used to detect incipient faults within power transformers. While the measurement accuracy has become relatively high since the development of various off-line and on-line measuring sensors, interpretation techniques of DGA results still depend on the level of personnel expertise more than analytical formulation. Therefore, various interpretation techniques may lead to different conclusions for the same oil sample. Moreover, ratio-based interpretation techniques may fail in interpreting DGA data in case of multiple fault conditions and when the oil sample comprises insignificant amount of the gases used in the specified ratios. This paper introduces an improved approach to overcome the limitations of conventional DGA interpretation techniques, automate and standardize the DGA interpretation process. The approach is built based on incorporating all conventional DGA interpretation techniques in one expert system to identify the fault type in a more consistent and reliable way. Gene Expression Programming is employed to establish this expert system. Results show that the proposed approach provides more reliable results than using individual conventional methods that are currently adopted by industry practice worldwide.

Fault Diagnosis and RUL Prediction of Nonlinear Mechatronic System via Adaptive Genetic Algorithm-Particle Filter

This paper proposes a real-time model-based health monitoring method for a nonlinear mechatronic system with multiple faults in both parametric and nonparametric components. A nonlinear bond graph model incorporating the influence of Stribeck friction is established to capture the dynamic behavior of the monitored mechatronic system. Based on the model, fault diagnosis is carried out via the combinative fault signature matrix which is built from independent and dependent analytical redundancy relations to enhance the isolation ability of the monitored system under multiple-fault condition. After that, an adaptive genetic algorithm-particle filter (AGA-PF) is developed for fault parameter estimation and remaining useful life prediction. The AGA-PF can mitigate the sample impoverishment problem in generic particle filter through genetic operators with adaptive mutation probability according to the fitness of the particles. The effectiveness of the proposed method is verified through simulation and experiment investigations on the nonlinear mechatronic system.

An Improved Algorithm for Power Distribution System Restoration Using Microgrids for Enhancing Grid Resiliency

Smart grid utilities all over the world aim at providing reliable and resilient power to its customers. Microgrids play a vital role in delivering resilient and reliable supply during major contingencies due to natural disasters. The resiliency of the power network is attributed to three features viz. prevention, survivability, and recovery. The improvement in any or all of these improves the overall resiliency of the system. This article presents an improved distribution system restoration algorithm, with microgrids for enhancing the survivability of out of service loads due to extreme events, like natural disasters. An optimal strategy to restore maximum loads with minimal switching operations under single and multiple fault conditions is proposed. Performance of the algorithm is tested on a modified IEEE 37-node system, and the results are compared with reported works. Moreover, a typical case study of a natural disaster due to Cyclone Roanu is considered, and the efficacy of the proposed algorithm is assessed.

A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing

Abstract Sensitive feature extraction from the raw vibration signal is still a great challenge for intelligent fault diagnosis of rolling bearing. Current fault classification framework generally concentrates on the pattern of classifier with single-domain feature, which is easy to induce insufficient feature extraction and low recognition accuracy. Therefore, to address this issue and improve intelligent diagnostic accuracy of rolling bearing, this paper proposes a novel fault classification algorithm based on optimized SVM with multi-domain feature, which mainly consists of three stages (i.e. multi-domain feature extraction, feature selection and fault identification). In this first stage, three approaches (i.e. statistical analysis, FFT and VMD) are separately applied to extract the fault feature information from multi-domain aspect (e.g. time-domain, frequency-domain and time-frequency domain), which can excavate comprehensively the condition information and intrinsic property of the raw vibration signal. Secondly, Laplace score algorithm is introduced to select automatically the meaningful sensitive feature according to the importance of each feature, which is aimed at removing some redundant information and improving the calculation efficiency. Finally, particle swarm optimization-based support vector machine (PSO-SVM) classification model is employed to implement the identification of multiple fault condition of rolling bearing. Performance of the proposed method is evaluated on two experimental examples of rolling bearing fault diagnosis. Experimental results show that the proposed method achieves high diagnosis accuracy for different working conditions of rolling bearing and outperforms some traditional methods both mentioned in this paper and published in other literature.

Model-based fault detection, fault isolation and fault-tolerant control of a blade pitch system in floating wind turbines

Abstract This paper presents model-based fault detection, fault isolation, and fault-tolerant control schemes focused on blade pitch systems in floating wind turbines. Fault detection, isolation, and accommodation techniques are required to achieve high power capture efficiency and structural reliability in floating wind turbines. Faults in blade pitch systems should be detected at an early stage to prevent catastrophic failures. To detect faults of the blade pitch systems, a Kalman filter is designed to estimate the blade pitch angle of the system. The fault isolation algorithm is based on inference methods and capable of determining the fault type, location, magnitude and time. The fault-tolerant controller based on a reconfiguration block with a virtual sensor and shutdown mode controls the floating wind turbine to avoid unexpected external loads. The proposed methods are demonstrated in case studies with stochastic wind and wave conditions that considering different types of faults, such as biases and fixed outputs in pitch sensors and stuck pitch actuators. The simulation results show that the proposed methods can detect and isolate multiple faults effectively at an early stage. Additionally, the effectiveness of the fault-tolerant control systems for different load cases for single and multiple fault conditions is verified by numerical simulations.

Investigation of Permanent Magnet Demagnetization in Synchronous Machines during Multiple Short-Circuit Fault Conditions

Faults in electrical machines can vary in severity and affect different parts of the machine. This study focuses on various kinds of short-circuits on the terminal side of a generic 20 kW surface mounted permanent magnet synchronous generator and how successive faults affect the performance of the machine. The study was conducted with the commercially available finite element method software COMSOL Multiphysics ® , and two time-dependent models for demagnetization of permanent magnets were compared, one using only internal models and the other using a proprietary external function. The study is simulation based and the two models were compared to a previously experimentally verified stationary model. Results showed that the power output decreased by more than 30% after five successive faults. In addition, the no-load voltage had become unsymmetrical, which was explained by the uneven demagnetization of the permanent magnets. The permanent magnet with the lowest reduction in average remanence was decreased by 0.8%, while the highest average reduction was 23.8% in another permanent magnet. The internal simulation model was about four times faster than the external model, but slightly overestimated the demagnetization.

A Self-Reconstructing Algorithm for Single and Multiple-Sensor Fault Isolation Based on Auto-Associative Neural Networks

Recently different approaches have been developed in the field of sensor fault diagnostics based on Auto-Associative Neural Network (AANN). In this paper we present a novel algorithm called Self reconstructing Auto-Associative Neural Network (S-AANN) which is able to detect and isolate single faulty sensor via reconstruction. We have also extended the algorithm to be applicable in multiple fault conditions. The algorithm uses a calibration model based on AANN. AANN can reconstruct the faulty sensor using non-faulty sensors due to correlation between the process variables, and mean of the difference between reconstructed and original data determines which sensors are faulty. The algorithms are tested on a Dimerization process. The simulation results show that the S-AANN can isolate multiple faulty sensors with low computational time that make the algorithm appropriate candidate for online applications.

An unsupervised artificial neural network versus a rule-based approach for fault detection and identification in an automated assembly machine

Artificial neural networks (ANNs) are suitable for fault detection and identification (FDI) applications because of their pattern recognition abilities. In this study, an unsupervised ANN based on Adaptive Resonance Theory (ART) is tested for FDI on an automated O-ring assembly machine testbed, and its performance and practicality are compared to a conventional rule-based method. Three greyscale sensors and two redundant limit switches are used as cost-effective sensors to monitor the machine's assembly process. Sensor data are collected while the machine is operated under normal condition, as well as 10 fault conditions. Features are selected from the raw sensor data, and data sets are created for training and testing the ANN. The performance of the ANN for detecting and identifying known, unknown and multiple faults is evaluated; the performance is compared to a conventional rule-based method using the same data sets. Results show that the ART ANN is able to achieve excellent fault detection performance with minimal modeling requirements; however, the performance depends on careful tuning of its vigilance parameter. Although the rule-based system requires more effort to set up, it is judged to be more useful when unknown or multiple faults are present. The ART network creates new outputs for unknown and multiple fault conditions, but it does not give any more information as to what the new fault is. By contrast, the rule-based method is able to generate symptoms that clearly identify the unknown and multiple fault conditions. Thus, the rule-based method is judged to be the most feasible method for FDI applications.

A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis

Abstract Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all current techniques rely on personnel experience more than analytical formulation. As a result, the current techniques do not necessarily lead to the same conclusion for the same oil sample. A significant number of DGA results fall outside the proposed codes of the ratio-based interpretation techniques and cannot be diagnosed using these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach that aids in standardizing DGA interpretation and identifies transformer critical ranking based on DGA data. The approach relies on incorporating all traditional DGA interpretation techniques (Roger, Doerenburg, IEC, key gas and Duval triangle methods) into one expert model. In this context, DGA results of 338 oil samples of pre-known fault conditions that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used first to analyze the DGA results to evaluate the consistency and accuracy of each method in identifying various faults. Results of this analysis were then used to develop the proposed fuzzy logic model. The model is validated using another set of DGA data that were collected form previously published papers.

Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques

This work presents a robust and computationally efficient algorithm for both whole-building and component-level energy fault detection and diagnosis (FDD). The algorithm is able to provide reliable estimation of multiple and simultaneous fault conditions, even in the presence of noisy and sometimes erroneous sensor data, and to provide uncertainty estimation. The algorithm can be used to provide such outputs as the probability of a fault, the likely cause(s), and the expected consequences of the fault(s) on energy use. The approach is based on an advanced Bayesian nonlinear state estimation technique called Unscented Kalman Filtering, but with our addition of a back-smoothing method that provides fast and robust FDD for common building use cases. The approach is presented and demonstrated for detecting energy and hydraulic faults in a chiller plant. The model of the chiller plant is a subsystem of an actual chiller plant, calibrated to real data. The algorithm can detect common faults, such as (1) energy faults (e.g., the chiller is not working properly, or far from its nominal condition), (2) functional faults caused by issues in the compressor and (3) occlusions in the valves that may reduce the water flow rate through the condenser and evaporator water loop. It is also shown that estimates of uncertainty are consistent with the error in the synthetic data, and can be updated as new data stream in from sensors.

A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis

Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all these techniques rely on personnel experience more than analytical formulation. As a result, various interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. Furthermore, significant number of DGA results fall outside the proposed codes of the current based-ratio interpretation techniques and cannot be diagnosed by these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach to reduce dependency on expert personnel and to aid in standardizing DGA interpretation techniques. The approach relies on incorporating all existing DGA interpretation techniques into one expert model. DGA results of 2000 oil samples that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used to analyze the collected DGA results to evaluate the consistency and accuracy of each interpretation technique. Results of this analysis were then used to develop the proposed fuzzy logic model.

Multiple fault condition recognition of gearbox with sequential hypothesis test

A novel method for the fault condition recognition in which the recognition system interrogates a propagation channel adaptively and intelligently by using the available data is proposed based on sequential hypothesis testing. The waveform of the data in the propagation channel for the fault condition recognition is designed with the kurtosis of the measured data in time domain. The sequential hypothesis testing framework is proposed when hard decisions are made with adequate confidence. The distinguished characteristic of the channel recognition is that it operates in a closed loop and makes constant optimization in response to its changing understanding of the channel. The fault condition recognition of the gearbox is to update the multiple target hypothesis/class based on the measured data, customize waveform as the class probabilities changes, and make conclusion when the sufficient understanding of the propagation channel is achieved.

Voltage Dip Analyzing Method for Multiple Faults Condition in Complex Power Network

This paper proposed a voltage dip analyzing method for multiple faults condition in complex power network. This method regarded the fault points as virtual buses and introduced multiple fault position parameters into voltage dip analysis. It expanded the node impedance matrix by calculating the self-impedance of the virtual bus, the mutual impedance between virtual bus and non-fault bus, one virtual bus and another. According to the faults boundary conditions, each fault current phasor was calculated. The fault voltage function of any bus related with the multiple fault position parameters in the power network was derived. The voltage dip analysis result was obtained associated with the voltage dip threshold. The proposed method was validated in the standard IEEE-30 bus system.

Enthalpy–entropy graph approach for the classification of faults in the main power system of a closed Brayton cycle HTGR

An enthalpy–entropy ( h– s) graph approach for the classification of faults in a new generation type high temperature gas-cooled reactor (HTGR) is presented. The study is performed on a 165 MW model of the main power system (MPS) of the pebble bed modular reactor (PBMR) that is based on a single closed-loop Brayton thermodynamic cycle. In general, the h– s graph is a useful tool in order to understand and characterize a thermodynamic process. It follows that it could be used to classify system malfunctions from fault patterns (signatures) based on a comparison between actual plant graphs and reference graphs. It is demonstrated that by applying the h– s graph approach, different fault signatures are derived for the examined fault conditions. The fault conditions that are considered for the MPS are categorized in three fault classes and comprise the main flow bypass of the working fluid, an increase in main flow resistance, and a decrease in component effectiveness or efficiency. The proposed approach is specifically illustrated for four single and two multiple fault conditions during normal power operation of the plant. The simulation of the faults suggests that it is possible to classify all of the examined system malfunctions correctly with the h– s graph approach, using only single reference fault signatures.

Improved robustness and sensor fault tolerance via a generalized internal model-based fault-tolerant controller

In this paper the synthesis of an implicit fault-tolerant control system that is capable of handling multiple sensor faults in addition to modelling uncertainties is presented. The implicit approach allows sensor fault tolerance without the need for accurate information from the fault diagnosis element. Difficulties encountered when solving for a fault-tolerant control problem commonly related to the reliability and accuracy of its fault diagnosis element are thus avoided. Based on extensions to the generalized internal model-based controller (GIMC), the new development proposed in this paper includes the introduction of an Hinfin loop-shaping controller in the structure to facilitate best control performance augmented by standard H∞ controllers as a fault-compensating block to facilitate fault tolerance towards multiple sensor fault conditions and robustness towards modelling uncertainties. Crucial stability and robustness analysis/proofs of the proposed design are conveniently obtained. Sensor faults are modelled as functions within a bounded family of signals and an internal model of this family is embedded into the fault compensating H∞ controller which is cascaded to the H∞ loop-shaping controller. The reliability of operation towards modelling uncertainties is also shown to be guaranteed. Its potential for providing multiple sensor fault tolerance in addition to robustness towards modelling uncertainties is illustrated with a flight control system example; results are significant, as observed in a comparison of control performance with standard H∞ and μ controllers.

The application of neural networks to vibrational diagnostics for multiple fault conditions

Vibration analysis has long been used for the detection and identification of machine fault conditions. The specific characteristics of the vibration spectrum that are associated with common fault conditions are quite well known, e.g. the BPOR spectral component reflecting bearing defects and the peak at the rotational frequency in the vibration spectrum indicating the degree of imbalance. The typical use of these features would be to determine when a machine should be taken out of operation in the presence of deteriorating fault conditions. Reliable diagnostics of deteriorating conditions may however be more problematic in the presence of simultaneous fault conditions. This paper demonstrates that the presence of a bearing defect makes it impossible to determine the degree of imbalance based on a single vibration feature, e.g. the peak at rotational frequency. In such a case, it is necessary to employ diagnostic techniques that are suited to the parallel processing of multiple features. Neural networks are the best known technique to approach such a problem. The paper demonstrates that a neural classifier using the X and Y components of both the peak at rotational frequency and the peak at BPOR frequency as input features can reliably diagnose the presence of bearing defect and can at the same time indicate the degree of imbalance. Several different neural techniques are evaluated for this purpose. It is first shown how Kohonen feature maps can be applied to do an exploratory analysis on the data, and to implement reliable classification of different bearing conditions. Different supervised neural classification techniques are then evaluated for their ability to reliably model the degree of imbalance, while also identifying the presence of defects.

Multiple Fault Detection and Isolation Using the Haar Transform, Part 2: Application to the Stamping Process

The sheet metal drawing operation is a complex manufacturing process involving more than forty process variables. The intricate interaction among these variables affect the forming tonnage which is measured by strain gages mounted on the press. A fault is said to occur when any of these process variables deviate beyond their specified limits. Current detection schemes based on thresholding do not fully exploit the information in the tonnage signals for the detection and isolation of multiple fault condition. It is thus an excellent case study for demonstrating the implementation of the detection methodology presented in Part 1. By partitioning the tonnage signature into disjoint segments, mutually exclusive sets of Haar coefficients can be used to isolate faults in each stage of the process.

A new approach to quantitative and credible diagnosis for multiple faults of components and sensors

Many practical applications of system diagnosis require the credible identification of multiple faults of nonlinear components and sensors in quantitative measures. However, the state of the art of diagnosis technique is considered to be still insufficient to meet these severe requirements. The approach of diagnosis using the traditional linear system identification theory can diagnose the disturbed parameters of a system in detail and evaluate the quantitative amplitude of the disturbance. However, it hardly provides the diagnosis of the multiple faults and the diagnosis of the components having high nonlinearity. On the other hand, some recent model-based diagnosis approaches can diagnose the multiple faults even for highly nonlinear components, though they do not provide the detailed diagnosis of elements indivisibly involved in components and the quantitative amplitudes of the faults. The method proposed in this paper provides an efficient remedy to achieve all of the practical requirements, i.e., the credible, detailed and quantitative diagnosis of multiple faults of nonlinear components and sensors. Our study newly proposes the frameworks of optimal constraints and causal ordering of physical systems. Also, a systematic and strict theory to synthesize these frameworks together with the model-based diagnosis is provided to characterize an optimal consistency checking method in diagnosis and to evaluate quantitative amplitudes of faulty disturbances. First, the detection of faulty behaviors of an objective component is performed based on the quantitative consistency checking between observations and the optimal constraints, called as “minimal overconstraints”, consisting of first principles in the components. Second, once if some inconsistencies are detected, a mathematical operation of model-based diagnosis derives the candidates of faulty elements and functions even under multiple fault conditions. Third, the anomalous quantities directly disturbed by the faulty elements are identified systematically based on causal ordering. Furthermore, the quantitative deviations of these quantities are evaluated by using the minimal over-constraints. The performance of the proposed method is demonstrated through an example to diagnose an electric water heater. The ability of this diagnosis has been confirmed for the multiple faults in nonlinear and dynamic systems.

A New Representation for Faults in Combinational Digital Circuits

A new representation for faults in combinational digital circuits is presented. Faults that are inherently indistinguishable are identified and combined into classes that form a geometric structure that effectively subdivides the original circuit into fan-out-free segments. This fan-out-free characteristic allows a simplified analysis of multiple fault conditions. For certain circuits, including all two-level single-output circuits, it is shown that the detection of all single faults implies the detection of all multiple faults. The behavior of any circuit under fault conditions is represented in terms of the classes of indistinguishable faults. This results in a description of the faulty circuit by means of Boolean equations that are readily manipulated for the purpose of fault simulation or test generation. A connection graph interpretation of this fault representation is discussed. Heuristic methods for the selection of efficient tests without extensive computation are derived from these connection graphs.