Multiple Fault Diagnosis Research Articles

A swarm-based multiple reduction approach for fault diagnosis

Fault diagnosis is a complex and difficult problem in the equipment maintenance. Whenever a fault symptom is detected, the system diagnosis is expected to carry out timely. It would be very helpful to improve the overall productivity. This paper presents a class of fault diagnosis problem, in which many items could be chosen in the diagnosis environment. Some of the data come from their experience or estimation. The information is redundant and inaccurate. Swarm-based rough set approach is introduced to make an attempt to solve the problem. Rough set theory provides a mathematical tool that can be used for both feature selection and information reduction. The swarm-based reduction approaches are attractive to find multiple reducts in the decision systems, which could be applied to generate multiple fault diagnosis planning and to improve diagnosis the decision. Empirical results illustrate that the approach can be applied for the class of fault diagnosis problems effectively.

Layered clustering multi-fault diagnosis for hydraulic piston pump

Efficient diagnosis is very important for improving reliability and performance of aircraft hydraulic piston pump, and it is one of the key technologies in prognostic and health management system. In practice, due to harsh working environment and heavy working loads, multiple faults of an aircraft hydraulic pump may occur simultaneously after long time operations. However, most existing diagnosis methods can only distinguish pump faults that occur individually. Therefore, new method needs to be developed to realize effective diagnosis of simultaneous multiple faults on aircraft hydraulic pump.In this paper, a new method based on the layered clustering algorithm is proposed to diagnose multiple faults of an aircraft hydraulic pump that occur simultaneously. The intensive failure mechanism analyses of the five main types of faults are carried out, and based on these analyses the optimal combination and layout of diagnostic sensors is attained. The three layered diagnosis reasoning engine is designed according to the faults' risk priority number and the characteristics of different fault feature extraction methods. The most serious failures are first distinguished with the individual signal processing. To the desultory faults, i.e., swash plate eccentricity and incremental clearance increases between piston and slipper, the clustering diagnosis algorithm based on the statistical average relative power difference (ARPD) is proposed. By effectively enhancing the fault features of these two faults, the ARPDs calculated from vibration signals are employed to complete the hypothesis testing. The ARPDs of the different faults follow different probability distributions. Compared with the classical fast Fourier transform-based spectrum diagnosis method, the experimental results demonstrate that the proposed algorithm can diagnose the multiple faults, which occur synchronously, with higher precision and reliability.

A Bayesian network approach for fixture fault diagnosis in launch of the assembly process

Verification and correction of faults related to tooling design and tooling installation are important in the auto body assembly process launch. This paper introduces a Bayesian network (BN) approach for quick detection and localisation of assembly fixture faults based on the complete measurement data set. Optimal sensor placement for effective diagnosis of multiple faults, structure learning of the Bayesian network and the diagnostic procedure are incorporated in the proposed approach. The effective independence sensor placement method is used to reach the desired number of optimal sensor locations, which provide the concise and effective sensor nodes to build the diagnostic Bayesian network. A new algorithm based on conditional mutual information tests is put forward to learn the Bayesian network structure. The body side assembly case was used to illustrate the suggested method and the simulation analysis was performed to evaluate the effectiveness of the diagnostic network. The work demonstrated that the proposed methodology composes a feasible and powerful tool for fixture fault diagnosis in launch of the assembly process.

Sensor Fault Detection, Diagnosis and Validation - A Survey

This paper provides an overview and analysis of data-driven sensor fault detection, diagnosis and validation from the application viewpoint. The typical sensor fault detection indices in the literature and the fundamental issues of necessary and sufficient conditions for detectability, reconstructability, identifiability and isolatability are analyzed. The main objective is to study the essential and important algorithms and techniques for single or multiple sensor fault diagnosis and validation. The issues of optimal principal components, sensor validity index, maximized sensitivity, as well as robust sensor fault diagnosis, etc. are discussed. Additional focuses are summarized at the end of the paper for future investigation.

Critical analysis of conventional thermoeconomic approaches to the diagnosis of multiple faults in air conditioning units: Capabilities, drawbacks and improvement directions. A case study for an air-cooled system with 120 kW capacity

Faults diagnosis in air conditioning systems is a crucial activity, since malfunctions may induce heavy degradation of performance. Among the available techniques, thermoeconomic diagnosis has played a marginal role due to the difficulties encountered when applying the methodology to refrigeration plants. In this paper a critical analysis on capabilities and limits of thermoeconomic diagnosis is proposed. The reference plant is a 120 kWc air-cooled air conditioning system; a simulator is used to evaluate thermodynamic data under normal and faulty conditions, setting up “virtual experiments” to simulate each fault. Five malfunctions are imposed, either individually or simultaneously: fouling at condenser and evaporator, refrigerant undercharge, compressor valve leakage and extra-superheating along suction line. From a qualitative viewpoint, the examined approach resulted efficient in identifying some faults, although false fault signals eventually emerged. Methodological improvements were suggested, to make thermoeconomic diagnosis capable of quantifying the additional energy consumption provoked by any specific fault.

Multiple Soft Fault Diagnosis of Nonlinear Circuits Using the Continuation Method

This paper offers a method for multiple soft fault diagnosis of nonlinear static circuits. The method enables us to locate the faulty elements and evaluate their parameters. It exploits a set of n nonlinear algebraic type diagnostic equations in n unknown variables and is oriented on finding multiple solutions of these equations. As a result, the method is capable of finding, in systematic manner, different sets of the parameters which satisfy the diagnostic test, rather than one specific set. For this purpose the continuation (homotopy) approach is applied and an efficient procedure for tracing a homotopy path is developed. The proposed method is especially useful at the pre-production stage, where corrections of the technological process are possible and the diagnostic time is not crucial. To illustrate the proposed approach two numerical examples are given.

Improving Multiple Fault Diagnosability using Possible Conflicts

Multiple fault diagnosis is a difficult problem for dynamic systems. Due to fault masking, compensation, and relative time of fault occurrence, multiple faults can manifest in many different ways as observable fault signature sequences. This decreases diagnosability of multiple faults, and therefore leads to a loss in effectiveness of the fault isolation step. We develop a qualitative, event-based, multiple fault isolation framework, and derive several notions of multiple fault diagnosability. We show that using Possible Conflicts, a model decomposition technique that decouples faults from residuals, we can significantly improve the diagnosability of multiple faults compared to an approach using a single global model. We demonstrate these concepts and provide results using a multi-tank system as a case study.

PWM-Switching pattern-based diagnosis scheme for single and multiple open-switch damages in VSI-fed induction motor drives

This paper deals with a fault detection technique for insulated-gate bipolar transistors (IGBTs) open-circuit faults in voltage source inverter (VSI)-fed induction motor drives. The novelty of this idea consists in analyzing the pulse-width modulation (PWM) switching signals and the line-to-line voltage levels during the switching times, under both healthy and faulty operating conditions. The proposed method requires line-to-line voltage measurement, which provides information about switching states and is not affected by the load. The fault diagnosis scheme is achieved using simple hardware and can be included in the existing inverter system without any difficulty. In addition, it allows not only accurate single and multiple faults diagnosis but also minimization of the fault detection time to a maximum of one switching period (Tc). Simulated and experimental results on a 3-kW squirrel-cage induction motor drive are displayed to validate the feasibility and the effectiveness of the proposed strategy.

High Resolution Application Specific Fault Diagnosis of FPGAs

High resolution diagnosis plays a critical role in silicon debug and yield improvement. Application-dependent diagnosis is also a key component in online testing and adaptive computing. In this paper, a new technique for high resolution localization of faults in the interconnects and logic blocks of an arbitrary design implemented on a field-programmable gate array (FPGA) is presented. This work is complementary to application-independent detection methods for FPGAs. This technique can uniquely identify any single bridging, open, or stuck-at fault in the interconnect as well as any single functional fault, a fault resulting a change in the truth table of a function, in the logic blocks. The number of test configurations for interconnect diagnosis is logarithmic to the size of the mapped design, whereas logic diagnosis is performed in only one test configuration with less than 5% overhead of built-in self diagnosis. These techniques have been further extended for multiple fault diagnosis.

A Quantum-inspired Evolutionary Algorithm with a competitive variation operator for Multiple-Fault Diagnosis

A heuristic search algorithm, the Quantum-inspired Competitive Evolutionary Algorithm (QuCEA), based on both quantum and evolutionary computing, is proposed. The individuals of a population, coded as qubit strings, evolve by means of an original variation operator inspired by competitive learning. The proposed operator is application independent and intuitively controllable by a single real parameter. QuCEA has been applied to Multiple-Fault Diagnosis, a typical NP-hard problem for industrial diagnosis. In particular, the proposed algorithm gives remarkable results both in simulation and in on-field tests for a lift monitoring system, also in comparison with a standard genetic algorithm and a state-of-the-art Quantum-inspired Evolutionary Algorithm.

Research on Multiple Simultaneous Faults Diagnosis of Hydropower Unit

The multiple simultaneous faults diagnosis is not deeply studied so far. The methods for multiple simultaneous faults diagnosis are put forward. According to the characteristics of hydropower unit, the hierarchical faults decoupling methods are discussed in detail. The simulation calculation of the cylinder controlled by valve is taken as an example for decoupling of hierarchical fault .

Multiple faults diagnosis in motion system based on SVM

In order to strengthen the safety and to monitor the working states of numerical control system real time, a framework was developed to diagnose multi-kinds of potential connection-related faults in the system. This framework considered that the number of fault data is small and the map from position to applied torque is a complex nonlinear function, and adopted the method of support vector machine to decide whether fault states had occurred or would occur. Position signal and the torque monitoring one in this system were used to train the parameters of support vector machine where Gaussian function was employed as nonlinear kernel. The faults mentioned in this research were diagnosed from a simple decision function real time, where the parameters were from the trained results. The framework and a trained decision function were applied to an X–Y motion platform, where the process included data acquisition, training of support vector machine and fault diagnosis. Results validate that three working states including two kinds of faults can be diagnosed easily at the same time, and the method can be used to numerical control system feasibly.

Measurement Selections for Multicomponent Gas Path Diagnostics Using Analytical Approach and Measurement Subset Concept

Gas path analysis (GPA) is a powerful tool to predict gas turbine degradations based on measurement parameters of gas turbine engines. Accordingly, prudent measurement selections are crucial to ensure accurate GPA predictions. This paper is intended to investigate the influence of measurement parameter selection toward the effectiveness of GPA algorithm. An analytical methodology for measurement selection, combined with measurement subset concept, is developed to properly select measurements for multiple component fault diagnosis. The effectiveness of GPA using the measurement sets selected with the introduced measurement selection method are then compared with the results of using standard measurements installed on existing gas turbine engines. A case study applying the new measurement selection method to GPA diagnostic analysis is demonstrated on a three-shaft aeroderivative industrial gas turbine model based on similar unit installed onboard an offshore platform operated by PETRONAS. The engine is modeled and simulated using PYTHIA, a gas turbine performance and diagnostics analysis tool developed by Cranfield University. To validate the findings, nonlinear GPA prediction errors are evaluated in various cases of single and multicomponents faults. As a result, the selected measurements have successfully produced much superior diagnostics accuracies in the fault cases when compared with the standard measurements. These findings proved that proper measurement selection for better GPA diagnostic analysis can be achieved by using the proposed analytical methods. Several engine sensor enhancements are also discussed to accommodate the unique sensor requirements for health diagnostics using GPA.

The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors

Induction motors are critical components for most industries. Induction motor failures may yield an unexpected interruption at the industry plant. Several conventional vibration and current analysis techniques exist by which certain faults in rotating machinery can be identified; however, they generally deal with a single fault only. Instead, in real induction machines, the case of multiple faults is common. When multiple faults exist, vibration and current are excited by several fault-related frequencies combined with each other, linearly or nonlinearly. Different techniques have been proposed for the diagnosis of rotating machinery in literature, where most of them are focused on detecting single faults and few works deal with the diagnosis and identification of multiple combined faults. The contribution of this paper is the development of a condition-monitoring strategy that can make accurate and reliable assessments of the presence of specific fault conditions in induction motors with single or multiple combined faults present. The proposed method combines a finite impulse response filter bank with high-resolution spectral analysis based on multiple signal classification for an accurate identification of the frequency-related fault. Results show the methodology potentiality as a deterministic detection technique that is suited for detecting multiple features where the fault-related frequencies are very close to those analytically reported in literature.

Multiple catastrophic fault diagnosis of analog circuits considering the component tolerances

SUMMARYThis paper deals with the diagnosis of multiple catastrophic faults, being cuts (open‐circuits) of some connecting paths and/or short‐circuits of some pairs of points in analog circuits. A method enabling us to detect and identify the faults, taking into account the deviations of the circuit parameters within their tolerance ranges, is developed. The method exploits an appropriate fault dictionary. The fault dictionary is used only for preliminary identification of the faults, because it is based on the analysis of the circuits with nominal parameters. The crucial point of the method is a verification procedure, proposed in this paper, based on the linear programming approach. It leads to the results considering the component variations within their tolerance ranges. In addition, a procedure for selecting appropriate test points, employing some evolutionary techniques, is developed. Although the approach presented in this paper is described in detail for linear circuits, it can be directly generalized to nonlinear circuits. Three numerical examples, including two linear and one nonlinear circuits, illustrate the proposed method and show its efficiency. Copyright © 2011 John Wiley & Sons, Ltd.

Multiple Soft Fault Diagnosis of Nonlinear DC Circuits Considering Component Tolerances

Multiple Soft Fault Diagnosis of Nonlinear DC Circuits Considering Component TolerancesThis paper is devoted to multiple soft fault diagnosis of analog nonlinear circuits. A two-stage algorithm is offered enabling us to locate the faulty circuit components and evaluate their values, considering the component tolerances. At first a preliminary diagnostic procedure is performed, under the assumption that the non-faulty components have nominal values, leading to approximate and tentative results. Then, they are corrected, taking into account the fact that the non-faulty components can assume arbitrary values within their tolerance ranges. This stage of the algorithm is carried out using the linear programming method. As a result some ranges are obtained including possible values of the faulty components. The proposed approach is illustrated with two numerical examples.

Simultaneous debugging of software faults

(Semi-)automated diagnosis of software faults can drastically increase debugging efficiency, improving reliability and time-to-market. Current automatic diagnosis techniques are predominantly of a statistical nature and, despite typical defect densities, do not explicitly consider multiple faults, as also demonstrated by the popularity of the single-fault benchmark set of programs. We present a reasoning approach, called Zoltar-M(ultiple fault), that yields multiple-fault diagnoses, ranked in order of their probability. Although application of Zoltar-M to programs with many faults requires heuristics (trading-off completeness) to reduce the inherent computational complexity, theory as well as experiments on synthetic program models and multiple-fault program versions available from the software infrastructure repository (SIR) show that for multiple-fault programs this approach can outperform statistical techniques, notably spectrum-based fault localization (SFL). As a side-effect of this research, we present a new SFL variant, called Zoltar-S(ingle fault), that is optimal for single-fault programs, outperforming all other variants known to date.

Selection of a Fault Model for Fault Diagnosis Based on Unique Responses

In this paper, we describe a preprocessing step to fault diagnosis of an observed response obtained from a faulty chip. In this step, a fault model for diagnosing the observed response is selected. This step allows fault diagnosis to be performed based on a single fault model after identifying the most appropriate one. We describe a specific implementation of this preprocessing step based on what is referred to as the unique output response of a fault model. As an example, we apply it to the diagnosis of multiple stuck-at faults, selecting between single and double stuck-at faults as the fault model for diagnosis. Experimental results demonstrate improvements compared to diagnosis based on single stuck-at faults, and compared to diagnosis based on both single and double stuck-at faults. We also discuss the use of a subset of double stuck-at faults for diagnosis, and the application of the proposed preprocessing step with other fault models.

Approximate Model-Based Diagnosis Using Greedy Stochastic Search

We propose a StochAstic Fault diagnosis AlgoRIthm, called SAFARI, which trades off guarantees of computing minimal diagnoses for computational efficiency. We empirically demonstrate, using the 74XXX and ISCAS-85 suites of benchmark combinatorial circuits, that SAFARI achieves several orders-of-magnitude speedup over two well-known deterministic algorithms, CDA* and HA*, for multiple-fault diagnoses; further, SAFARI can compute a range of multiple-fault diagnoses that CDA* and HA* cannot. We also prove that SAFARI is optimal for a range of propositional fault models, such as the widely-used weak-fault models (models with ignorance of abnormal behavior). We discuss the optimality of SAFARI in a class of strong-fault circuit models with stuck-at failure modes. By modeling the algorithm itself as a Markov chain, we provide exact bounds on the minimality of the diagnosis computed. SAFARI also displays strong anytime behavior, and will return a diagnosis after any non-trivial inference time.

Multiple sensor fault diagnosis for dynamic processes

Modern industrial plants are usually large scaled and contain a great amount of sensors. Sensor fault diagnosis is crucial and necessary to process safety and optimal operation. This paper proposes a systematic approach to detect, isolate and identify multiple sensor faults for multivariate dynamic systems. The current work first defines deviation vectors for sensor observations, and further defines and derives the basic sensor fault matrix (BSFM), consisting of the normalized basic fault vectors, by several different methods. By projecting a process deviation vector to the space spanned by BSFM, this research uses a vector with the resulted weights on each direction for multiple sensor fault diagnosis. This study also proposes a novel monitoring index and derives corresponding sensor fault detectability. The study also utilizes that vector to isolate and identify multiple sensor faults, and discusses the isolatability and identifiability. Simulation examples and comparison with two conventional PCA-based contribution plots are presented to demonstrate the effectiveness of the proposed methodology.