Tools for Evaluating Fault Detection and Diagnostic Methods for HVAC Secondary Systems

Principles of Classification

Diagnostics and Prognostics of Engineering Systems ◽

10.4018/978-1-4666-2095-7.ch003 ◽

2013 ◽

pp. 55-73

Author(s):

Veli Lumme

Keyword(s):

Fault Detection ◽

Software Development ◽

Anomaly Detection ◽

Diagnostic Methods ◽

Continuous Learning ◽

Sample Classification ◽

Secondary Objective ◽

Learning Machine ◽

Diagnostics System ◽

Effective Use

This chapter discusses the main principles of the creation and use of a classifier in order to predict the interpretation of an unknown data sample. Classification offers the possibility to learn and use learned information received from previous occurrences of various normal and fault modes. This process is continuous and can be generalized to cover the diagnostics of all objects that are substantially of the same type. The effective use of a classifier includes initial training with known data samples, anomaly detection, retraining, and fault detection. With these elements an automated, a continuous learning machine diagnostics system can be developed. The main objective of such a system is to automate various time intensive tasks and allow more time for an expert to interpret unknown anomalies. A secondary objective is to utilize the data collected from previous fault modes to predict the re-occurrence of these faults in a substantially similar machine. It is important to understand the behaviour and functioning of a classifier in the development of software solutions for automated diagnostic methods. Several proven methods that can be used, for instance in software development, are disclosed in this chapter.

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PWM-VSI Diagnostic and Reconfiguration Method Based on Fuzzy Logic Approach for SSTPI-Fed IM Drives under IGBT OCFs

Mathematical Problems in Engineering ◽

10.1155/2021/9505845 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Mohamed Ali Zdiri ◽

Mohsen Ben Ammar ◽

Fatma Ben Salem ◽

Hsan Hadj Abdallah

Keyword(s):

Fuzzy Logic ◽

Fault Detection ◽

Diagnostic Method ◽

Operating Conditions ◽

Drive System ◽

Diagnostic Methods ◽

Detection And Identification ◽

Efficient Detection ◽

Fault Detection And Identification ◽

Fuzzy Logic Technique

Due to the importance of the drive system reliability, several diagnostic methods have been investigated for the SSTPI-IM association in the literature. Based on the normalized currents and the current vector slope, this paper investigates a fuzzy diagnostic method for this association. The fuzzy logic technique is appealed in order to process the diagnosis variable symptoms and the faulty IGBT information. Indeed, the design, inputs, and rules of the fuzzy logic are distinct compared with the other existing diagnostic methods. The proposed fuzzy diagnostic method allows the best efficient detection and identification of the single and phase OCF of the SSTPI-IM association. Accordingly, after the fault detection and identification using this proposed FLC diagnostic method, a reconfiguration step of IGBT OCFs must be applied in order to compensate for these faults and ensure the drive system continuity. This reconfiguration is based on the change of the SSTPI-IM topology to the FSTPI-IM topology by activating or deactivating the used relays. Several simulation results utilizing a direct RFOC controlled SSTPI-IM drive system are investigated, showing the fuzzy diagnostic and reconfiguration methods’ performances, their robustness, and their fast fault detection during distinct operating conditions.

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Intermittent Arc Fault Detection And Location For Aircraft Power Distribution System

10.32920/ryerson.14646090 ◽

2021 ◽

Author(s):

Anil Yaramasu

Keyword(s):

Fault Detection ◽

Power Systems ◽

Power Distribution ◽

Distribution System ◽

Fault Location ◽

Diagnostic Methods ◽

Power Distribution System ◽

Matrix Modeling ◽

Non Destructive ◽

Load Circuit

This thesis addresses a non-destructive diagnostic method for intermittent arc fault detection and location. Intermittent arc faults appear in aircraft power systems in unpredictable manners when the degraded wires are wet, vibrating against metal structures, or under mechanical stresses, etc. They could evolve into serious faults that may cause on-board fires, power interruptions, system damage and catastrophic incidents, and thus have raised much concern in recent years. Recent trends in solid state power controllers (SSPCs) motivated the development of non-destructive diagnostic methods for health monitoring of aircraft wiring. In this thesis, the ABCD matrix (or transmission matrix) modeling method is introduced to derive normal and faulty load circuit models with better accuracy and reduced complexity compared to the conventional differential equation approach, and an intermittent arc fault detection method is proposed based on temporary deviations of load circuit model coefficients and wiring parameters. Furthermore, based on the faulty wiring model, a genetic algorithm (GA) is proposed to estimate the fault-related wiring parameters, such as intermittent arc location and average intermittent arc resistance. The proposed method can be applied to both the alternating current (AC) power distribution system (PDS) and direct current (DC) PDS. Simulations and experiments using a DC power source have been conducted, and the results have demonstrated effectiveness of the proposed method by estimating the fault location with an accuracy of +/- 0.5 meters on 24.6 meters wire. Unlike the existing techniques which generally requires special devices, the proposed method only needs circuit voltage and current measurement at the source end as inputs, and is thus suitable for SSPC-based aircraft PDS.

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A survey on diagnostic methods for automotive engines

International Journal of Engine Research ◽

10.1177/1468087411422851 ◽

2011 ◽

Vol 13 (1) ◽

pp. 41-64 ◽

Cited By ~ 38

Author(s):

J Mohammadpour ◽

M Franchek ◽

K Grigoriadis

Keyword(s):

Fault Detection ◽

Fault Detection And Diagnosis ◽

Diagnostic Methods ◽

Fault Detection And Isolation ◽

State Regulations ◽

Automotive Engine ◽

Automotive Engines ◽

Detection And Diagnosis ◽

Negative Impacts ◽

Component Faults

Faults affecting automotive engines can potentially lead to increased emissions, increased fuel consumption, or engine damage. These negative impacts may be prevented or at least alleviated if faults can be detected and isolated in advance of a failure. United States Federal and State regulations dictate that automotive engines operate with high-precision onboard diagnosis (OBD) systems that enable the detection of faults, resulting in higher emissions that exceed standard thresholds. In this paper, we survey and discuss the different aspects of fault detection and diagnosis in automotive engine systems. The paper collects some of the efforts made in academia and industry on fault detection and isolation for a variety of component faults, actuator faults, and sensor faults using various data-driven and model-based methods.

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Lab test of three fault detection and diagnostic methods’ capability of diagnosing multiple simultaneous faults in chillers

Energy and Buildings ◽

10.1016/j.enbuild.2015.02.039 ◽

2015 ◽

Vol 94 ◽

pp. 43-51 ◽

Cited By ~ 16

Author(s):

Xinzhi Zhao

Keyword(s):

Fault Detection ◽

Diagnostic Methods ◽

Lab Test

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Intermittent Arc Fault Detection And Location For Aircraft Power Distribution System

10.32920/ryerson.14646090.v1 ◽

2021 ◽

Author(s):

Anil Yaramasu

Keyword(s):

Fault Detection ◽

Power Systems ◽

Power Distribution ◽

Distribution System ◽

Fault Location ◽

Diagnostic Methods ◽

Power Distribution System ◽

Matrix Modeling ◽

Non Destructive ◽

Load Circuit

This thesis addresses a non-destructive diagnostic method for intermittent arc fault detection and location. Intermittent arc faults appear in aircraft power systems in unpredictable manners when the degraded wires are wet, vibrating against metal structures, or under mechanical stresses, etc. They could evolve into serious faults that may cause on-board fires, power interruptions, system damage and catastrophic incidents, and thus have raised much concern in recent years. Recent trends in solid state power controllers (SSPCs) motivated the development of non-destructive diagnostic methods for health monitoring of aircraft wiring. In this thesis, the ABCD matrix (or transmission matrix) modeling method is introduced to derive normal and faulty load circuit models with better accuracy and reduced complexity compared to the conventional differential equation approach, and an intermittent arc fault detection method is proposed based on temporary deviations of load circuit model coefficients and wiring parameters. Furthermore, based on the faulty wiring model, a genetic algorithm (GA) is proposed to estimate the fault-related wiring parameters, such as intermittent arc location and average intermittent arc resistance. The proposed method can be applied to both the alternating current (AC) power distribution system (PDS) and direct current (DC) PDS. Simulations and experiments using a DC power source have been conducted, and the results have demonstrated effectiveness of the proposed method by estimating the fault location with an accuracy of +/- 0.5 meters on 24.6 meters wire. Unlike the existing techniques which generally requires special devices, the proposed method only needs circuit voltage and current measurement at the source end as inputs, and is thus suitable for SSPC-based aircraft PDS.

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Development of Compartment Models for Diagnostic Purposes

Hungarian Journal of Industry and Chemistry ◽

10.33927/hjic-2021-07 ◽

2021 ◽

Vol 49 (1) ◽

pp. 47-58

Author(s):

Bálint Levente Tarcsay ◽

Ágnes Bárkányi ◽

Tibor Chován ◽

Sándor Németh

Keyword(s):

Fault Detection ◽

Expert Systems ◽

Human Life ◽

Complex Model ◽

Diagnostic Methods ◽

Detection Methods ◽

Environmental Damage ◽

Financial Loss ◽

Compartment Models ◽

Box Models

The importance of recognizing the presence of process faults and resolving these faults is continuously increasing parallel to the development of industrial processes. Fault detection methods which are both robust and sensitive help to recognize the presence of faults in time to avoid malfunctions, financial loss, environmental damage or loss of human life. In the literature, the use of various model-based fault detection methods has gained a considerable degree of popularity. Methods usually based on black-box models, data-based techniques or models using symbolic logic, e.g.\ expert systems, have become widespread. White-box models, on the other hand, have been applied less despite their considerable robustness because of multiple reasons. Firstly, their complexity and the relatively vast amount of technological and modelling knowledge needed to construct them for industrial systems. Secondly, their large computational demand which makes them less suitable for online fault detection. In this study, the aim was to resolve these problems by developing a method to simplify the complex Computational Fluid Dynamics models employed to describe the equipment used in the chemical industry into less complex model structures. These simpler structures are Compartment Models, a type of white-box model which breaks down a complex system into smaller units with idealized behaviour. In the case of a small number of compartments, the computational load of such models is not significant, therefore, they can be employed for the purposes of online fault detection while providing an accurate representation of the system. For the purpose of identifying the compartmental structure, fuzzy logic was employed to create a model which approximates the real behaviour of the system as accurately as possible. Our future objective is to explore the possibility of combining this model with various diagnostic methods (expert systems, Bayesian networks, parity relations, etc.) and derive robust tools for the purpose of fault detection.

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