Level Fault Diagnosis Research Articles

Non-inclusive g-extra diagnosability of interconnection networks under PMC model

System level fault diagnosis theory is of great weight in the design and maintenance of multiprocessor systems. In this paper, we propose a novel kind of diagnosability, called non-inclusive g-extra diagnosability, that requires all faulty sets to be non-inclusive and every connected fault-free subgraph has at least g+1 vertices. Furthermore, we determine the non-inclusive g-extra diagnosability of a class of graphs under PMC model. As applications, the non-inclusive g-extra diagnosability of (n,k)-star graph Sn,k, data center networks DCell (Dk,n), BCDC (Bn) and BCube (BCn,k) under PMC model are determined successively.

Integrated building fault detection and diagnosis using data modeling and Bayesian networks

Heating, ventilation, and air-conditioning (HVAC) equipment faults and operational errors result in comfort issues and waste of energy in buildings. An Automatic Fault Detection and Diagnosis (AFDD) tool could help facility managers fix comfort and energy issues more efficiently, by identifying the most probable root causes. Existing AFDD methods mostly focus on equipment-level fault detection and diagnostics; almost no attention is given to building level fault diagnosis, considering inter-dependency between equipment through the energy distribution chain. In this work we propose a methodology to automatically derive a Bayesian network from HVAC system topology description such as Haystack. This Bayesian network models and estimates the state of all elements in the system, helping users to identify the most probable root fault. As it is able to ingest evidence from any source (field data, operators, or other models) and is capable of updating its estimates when new evidence is delivered, such a tool could have a great potential to be used interactively on the field. We applied the proposed methodology on simulated and real-world buildings and present in this paper one specific case.

The partial diagnosability of interconnection networks under the Hybrid PMC model

System level diagnosis is a primary method to identify the faulty elements in a multiprocessor system. Different from the traditional system level fault diagnosis models, the new fault diagnosis model, hybrid PMC model (short for HPMC model), is suitable to the circumstances that the processor and link faults occur simultaneously. Under the HPMC model, the q-edge restricted diagnosability of a system H, denoted by tqe(H), is the maximum number p such that H can correctly identify all the faulty elements containing p faulty processors and at most q faulty links. Similarly, the p-vertex restricted edge-diagnosability of a system H, denoted by spv(H), is the maximum number q such that H can correctly identify all the faulty elements containing q faulty links and at most p faulty processors. These two diagnosability are collectively referred to as partial diagnosability. We investigate and determine the q-edge restricted diagnosability of a system H under the HPMC model for q≥η(H), where η(H)=max⁡{|N(u)∩N(v)||uv∈E(H)}. In addition, we investigate the relationship between tqe(H) and spv(H) under the HPMC model, and show that under some conditions, spv(H)=q if tqe(H)=p. Applying our results, the two kinds of partial diagnosability of k-ary n-cubes, bijective connection networks and undirected Kautz graphs are established.

Hierarchical level fault detection and diagnosis of ship engine systems

As smart ships are developed, research into engine management through remote monitoring and support from data collected at onshore control centers is being conducted. However, ship engine data collected from various sensors have high dimensions, large measurement errors, few labeled data, and insufficient amounts and quality of data, making it difficult to determine the condition of engines with complex failure modes under various operating environments.This study proposes a hierarchical level fault detection and diagnosis (HL-FDD) method that combines domain knowledge of ship engines and advanced data analysis techniques. The developed method extracts key features of reduced dimensions from the original variables (sensors) through an optimal hierarchical clustering and dimension-reduction model, allowing a hierarchy divided into the top (the entire system combining all features), middle (subsystems and feature sets), and bottom (components and sensors) levels. The reduced key features are used to generate robust regression models and dynamic thresholds (prediction intervals) according to the engine load, and dynamic thresholds determine whether the engine’s condition is normal or abnormal. The dynamic thresholds are able to automatically label abnormal conditions of the engine. Once anomalies are detected at the top level, the proposed method can sequentially search for data on features belonging to the middle and bottom hierarchies for detailed fault diagnosis to determine which engine subsystem or component(s) caused the engine fault(s). Actual data collected by ship operators verify the proposed method’s efficiency, reliability, and accuracy.

A hierarchical method based on improved deep forest and case-based reasoning for railway turnout fault diagnosis

Railway turnout system (RTS), which is widely laid along the railway tracks, is one of the most crucial devices in the whole railway infrastructures. Unpredictable outdoor weather, complex site conditions, and mechanical wear make the RTS the most vulnerable asset. Scientific and practical fault diagnosis is of great significance for reducing railway failures, improving operation efficiency, and ensuring transportation safety. In this paper, a hierarchical fault diagnosis method is proposed to realize accurate fault diagnosis of railway turnouts and improve the reliability and safety of railway systems. At the first level, an improved deep forest that embeds priori knowledge is adopted to distinguish the first-class fault sets. The embedded priori knowledge can help to outperform the traditional gcForest with fewer features produced at each level of the cascade forest. At the second level, a Case-based Reasoning component is utilized to classify the sub-class fault sets, which are difficult to be distinguished by the first level fault diagnosis due to their similar features. The hierarchical fault diagnosis method has been validated by using a real-world railway maintenance dataset. Extensive experiments show that the proposed method achieves superior performance compared to the state-of-the-art approaches in diagnostic precision under the constraint of limited data.

Characterization of Diagnosabilities on the Bounded PMC Model

Abstract In this paper, we propose a new digragh model for system level fault diagnosis, which is called the $(f_1,f_{2})$-bounded Preparata–Metze–Chien (PMC) model (shortly, $(f_1,f_{2})$-BPMC). The $(f_1,f_{2})$-BPMC model projects a system such that the number of faulty processors that test faulty processors with the test results $0$ does not exceed $f_{2}$$(f_2\leq f_{1})$ provided that the upper bound on the number of faulty processors is $f_{1}$. This novel testing model compromisingly generalizes PMC model (Preparata, F.P., Metze, G. and Chien R.T. (1967) On the connection assignment problem of diagnosable systems. IEEE Tran. Electron. Comput.,EC-16, 848–854) and Barsi–Grandoni–Maestrini model (Barsi, F., Grandoni, F. and Maestrini, P. (1976) A theory of diagnosability of digital systems. IEEE Trans. Comput.C-25, 585–593). Then we present some characterizations for one-step diagnosibility under the $(f_1,f_{2})$-bounded PMC model, and determine the diagnosabilities of some special regular networks. Meanwhile, we establish the characterizations of $f_1/(n-1)$-diagnosability and three configurations of $f_1/(n-1)$-diagnosable system under the $(f_1,f_{2})$-BPMC model.

Development and Field Evaluation of Data-driven Whole Building Fault Detection and Diagnosis Strategy

Faults, i.e., malfunctioned sensors, components, control, and systems, in a building have significantly adverse impacts on the building’s energy consumption and indoor environment. To date, extensive research has been conducted on the development of component level fault detection and diagnosis (FDD) for building systems, especially the Heating, Ventilating, and Air Conditioning (HVAC) system. However, for faults that have multi-system impacts, component level FDD tools may encounter high false alarm rate due to the fact that HVAC subsystems are often tightly coupled together. Hence, the detection and diagnosis of whole building faults is the focus of this study. Here, a whole building fault refers to a fault that occurs in one subsystem but triggers abnormalities in other subsystems and have significant adverse whole building energy impact. The wide adoption of building automation systems (BAS) and the development of machine learning techniques make it possible and cost-efficient to detect and diagnose whole building faults using data-driven methods. In this study, a whole building FDD strategy which adopts weather and schedule information based pattern matching (WPM) method and feature based Principal Component Analysis (FPCA) for fault detection, as well as Bayesian Networks (BNs) based method for fault diagnosis is developed. Fault tests are implemented in a real campus building. The collected data are used to evaluate the performance of the proposed whole building FDD strategies.

Modeling of PV system based on experimental data for fault detection using kNN method

In this paper, a string level fault detection and diagnosis technique for photovoltaic (PV)systems based on k-nearest neighbors (kNN) rule is proposed. It detects and classifies open circuit faults, line-line (L-L) faults, partial shading with and with-out bypass diode faults and partial shading with inverted bypass diode faults in real time. A detailed modeling of the PV systems based on experimental data is presented that only requires available data from the manufacturer’s datasheet reported under standard test conditions (STC) and normal operating cell temperature(NOCT). This model considers the temperature dependent variables such as junction thermal voltage Vt, diode quality factor (A) and series resistance(Rs). Simulations of the developed model have been carried out using Matlab/Simulink. A PV analyzer (Solar I-V) of HT instruments is used to measure the I(V)characteristics of PV module. The developed model precisely traces theI(V)characteristics of PV systems at different irradiance and temperature levels. The simulation results indicate that the error between the measured data and developed model is less than the models available in the literature. The absolute error is confined in the range 0.61 to 6.5%. Finally, the data generated from proposed model and experimental setup are used to validate and test the performance of the proposed fault detection and classificationFDC technique. It is observed from the results that the average of fault classification gives a high accuracy of 98.70%.

Transistor level fault diagnosis in digital circuits using artificial neural network

In the design of digital circuits, transistor level faults occur due to open or shorted connection in the transistor terminals and with the variations in the transistor parameters. In this study fault diagnosis for hard faults in the digital circuits using artificial neural network and virtual instrument is presented. During the diagnosis process the parametric variations in transistors are also taken into account by varying the threshold voltages of the transistors. The output responses of the circuit under test under faulty and fault free conditions are plotted for all the input combinations. The resulting responses are curve fitted using polynomial curve fitting. The polynomial coefficients are used as signatures values to train the back propagation artificial neural network, which in turn is used for fault classification. The virtual instrument is designed to implement the fault diagnosis system. The system is validated with experiments on universal gates and all the proposed faults are correctly diagnosed.

인휠 독립 구동 전기 자동차의 구동 모터 통합 고장 진단 알고리즘

This paper presents an integrated fault diagnosis algorithm for driving motor of In-wheel independent drive electric vehicle. Especially, this paper proposes a method that integrated the high level fault diagnosis and the low level fault diagnosis in order to improve a robustness and performance of the fault diagnosis system. The high level fault diagnosis is performed using the vehicle dynamics analysis and the low level fault diagnosis is carried using the motor system analysis. The validity of the high level fault diagnosis algorithms was verified through Carsim® and MATLAB/ Simulink® cosimulation and the low level fault diagnosis's validity was shown by applying it to a MATLAB/ Simulink® interior permanent magnet synchronous motor control system. Finally, this paper presents a fault diagnosis strategy by combining the high level fault diagnosis and the low level fault diagnosis.

Module level fault diagnosis for analog circuits based on system identification and genetic algorithm

This paper proposes a new module level fault diagnosis method for analog circuits. Firstly, the transfer function is constructed according to the relationship between output and input of the circuit under test (CUT). Every system parameter of the transfer function is expressed by several component parameters. These components are divided into several modules. Then, the way of objective function optimization based on genetic algorithm (GA) is adopted to solve nonlinear equations, which are obtained by multi-frequency testing. Finally, the module level faults are detected by comparing the estimated system parameters to their normal values. The results show that the proposed method is effective to identify system parameters and locate module level faults.

Hierarchical Fault Diagnosis and Fuzzy Rule-Based Reasoning for Satellites Formation Flight

Formation flying is an emerging area in the Earth and space science and technology domains that utilize multiple inexpensive spacecraft by distributing the functionalities of a single platform spacecraft among miniature inexpensive platforms. Traditional spacecraft fault diagnosis and health monitoring practices involve around-the-clock monitoring, threshold checking, and trend analysis of a large amount of telemetry data by human experts that do not scale well for multiple space platforms. A novel hierarchical fault diagnosis framework and methodology is presented here that enables a systematic utilization of fuzzy rule-based reasoning to enhance the level of autonomy achievable in fault diagnosis at ground stations. Fuzzy rule-based fault diagnosis schemes for satellite formation flight are developed and investigated at different levels in the hierarchy for a leader-follower architecture. Our formation level fault diagnosis is found to be useful as a supervisory diagnosis scheme that can prompt the operators to have a closer look at the potential faulty components to determine the sources of a fault. Effectiveness of our proposed fault diagnosis methodology is demonstrated by utilizing synthetic formation flying data of five satellites that are configured in the leader-follower architecture, and are subjected to nonabrupt intermittent faults in the attitude control subsystem (ACS) and the electrical power subsystem (EPS) of the follower satellites.

Hierarchical physical defect reasoning in digital circuits

We propose a hierarchical physical defect-oriented approach for fault diagnosis in combinational digital circuits. We present the circuit as a network of modules. As modules we consider either library components (e.g. complex gates) of digital circuits or arbitrary subcircuits. The higher level fault diagnosis is carried out in two phases. In the first phase, faulty modules are located by cause-effect analysis using high-level faulty module dictionary. The size of the dictionary depends linearly on the number of modules in the circuit. In the second phase, the set of suspected faulty modules is pruned by reasoning of the defective behaviour. At the lower level, the physical defects are directly located in suspected faulty modules using defect libraries of the modules or by effect-cause reasoning inside the module. The proposed approach to fault diagnosis helps to cope with the growing complexities of digital circuits. The experimental results show high module-level diagnostic resolution of the proposed approach.

Study on the Fault Diagnosis Technique of Aerial Camera Exposure Board Based on Petri Net

Because of the mechanical situation, aerial carrier condition, environment condition and usage maintenance of the aerial camera, faults always occur. So it is necessary to check and maintain the aerial camera on the ground, and the interior structure of the aerial camera is very complex, and the aerial camera is composed by many circuit boards, and the checking must be performed from the whole camera to each component. Aiming at the demands of aerial camera fault diagnosis, the board level fault diagnosis system of aerial camera was developed in the article. The failure mode of the exposure board in the aerial camera electric control system was completely analyzed, and the exposure board fault board model based on Petri net was established, and the dynamical transition of the system status was solved by the relational matrix method, and the TPS program of the exposure board was compiled, and the fault diagnosis of the exposure board was primarily actualized in the article.

04/01537 Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks: Lee, W.-Y. et al. Applied Energy, 2004, 77, (2), 153–170

04/01537 Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks: Lee, W.-Y. et al. Applied Energy, 2004, 77, (2), 153–170

Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks

This paper describes a scheme for on-line fault detection and diagnosis (FDD) at the subsystem level in an Air-Handling Unit (AHU). The approach consists of process estimation, residual generation, and fault detection and diagnosis. Residuals are generated using general regression neural-network (GRNN) models. The GRNN is a regression technique and uses a memory-based feed forward network to produce estimates of continuous variables. The main advantage of a GRNN is that no mathematical model is needed to estimate the system. Also, the inherent parallel structure of the GRNN algorithm makes it attractive for real-time fault detection and diagnosis. Several abrupt and performance degradation faults were considered. Because performance degradations are difficult to introduce artificially in real or experimental systems, simulation data are used to evaluate the method. The simulation results show that the GRNN models are accurate and reliable estimators of highly non-linear and complex AHU processes, and demonstrate the effectiveness of the proposed method for detecting and diagnosing faults in an AHU.

Cellular-automata-array-based diagnosis of board level faults

A novel scheme for board level fault diagnosis based on cellular automata array (CAA) is presented. In the proposed diagnosis scheme, the output responses of the chips are encoded by applying the strategy of CA-based byte error correcting code. The encoded response symbols for different test vectors are compressed to a signature. The decoding scheme of byte error correcting code is subsequently employed to detect the faulty chips on the board. The CAA-based design for the fault diagnosis strategy results in a simple and modular test structure that is well suited for VLSI implementation. The scheme can be applied for testing and diagnosis of multichip modules (MCMs) as well.

Sequentially t-diagnosable systems: a characterization and its applications

In the system level fault diagnosis area, the fundamental problem of characterizing sequentially t-diagnosable systems in the PMC model has remained open for more than two decades. We resolve this problem by providing a complete characterization of such systems. Our solution to the characterization problem leads to the correct identification of optimal sequentially t-diagnosable D/sub /spl delta/,k/ systems. Given a set of n units where n=2t+1, an optimal D/sub /spl delta/,k/ system can be constructed with just n([(t+2)/3]) tests, rather than n([t/2]+1) tests-a previously misjudged bound. An efficient algorithm for identifying the set of faulty units in a sequentially t-diagnosable D/sub /spl delta/,k/ system is given along the line of the proposed characterization, which is linear with respect to the number of tests in the system. >

Distributed fault diagnosis in multistage network-based multiprocessors

This paper is concerned with a distributed, system level fault diagnosis scheme for multistage network-based multiprocessors. The target system, which we choose as a representative, employs a multistage interconnection network with 4/spl times/4 switching elements. We propose a fast diagnostic method which uses a quadtree and its coupler structure. These two quadtree structures partition the system into a number of link-independent groups. This partitioning provides an important diagnostic property; the communication paths in each link-independent group are either identical or disjoint. Several previous works in fault diagnosis investigated the multistage interconnection network only. This paper presents an entire multiprocessor diagnosis, including the detection and location of single faults caused by processor nodes, switching elements, and communication links. In addition, the diagnosis of a group of multiple faults partitioned by the tree structures is also discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Fault Detection and Diagnosis of Automated Manufacturing Systems

In this paper, we propose and develop a two level scheme for monitoring and diagnosis in automated manufacturing systems. At the first level, there are dedicated diagnostic systems for each of the subsystems such as machine centres, robots, conveyors, etc. At the second level we have an intelligent controller monitoring the part flow and coordinating the local diagnostic systems and controllers. We assume that local controller and diagnostic systems exist for subsystem level fault detection and diagnosis, and present a Petri net based intelligent controller for system level fault detection and diagnosis.