Residual generation and statistical pattern recognition for engine misfire diagnostics

Osburn, Andrew W.; Kostek, Theodore M.; Franchek, Matthew A.

doi:10.1016/j.ymssp.2005.06.002

Cited by 30 publications

(16 citation statements)

References 17 publications

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“…Variations between different cylinders depending on their position was also observed in [20]. However, since the shape of the signal is always similar for each firing cylinder respectively, this can be compensated for when designing the test quantity by considering each cylinder separately.…”

Section: Misfire Detection Using Estimated Torquementioning

confidence: 95%

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Development of misfire detection algorithm using quantitative FDI performance analysis

Jung

Eriksson

Frisk

et al. 2015

Control Engineering Practice

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A model-based misfire detection algorithm is proposed. The algorithm is able to detect misfires and identify the failing cylinder during different conditions, such as cylinder-to-cylinder variations, cold starts, and different engine behavior in different operating points. Also, a method is proposed for automatic tuning of the algorithm based on training data. The misfire detection algorithm is evaluated using data from several vehicles on the road and the results show that a low misclassification rate is achieved even during difficult conditions.

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Section: Misfire Detection Using Estimated Torquementioning

confidence: 95%

“…• [20] and 90 • [19]). High resolution data gives more information about each combustion but requires more computational power and is also more sensitive to measurement errors caused by flywheel manufacturing errors and signal sampling resolution [19].…”

Section: Introductionmentioning

confidence: 99%

Development of misfire detection algorithm using quantitative FDI performance analysis

Jung

Eriksson

Frisk

et al. 2015

Control Engineering Practice

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“…These methods usually require additional sensors that are not generally available in the vehicle. Therefore, an available signal that is commonly used to detect misfires is the crankshaft angular velocity measured at the flywheel (Williams, 1996;Osburn et al, 2006;Naik, 2004;Tinaut et al, 2007).…”

Section: Approaches To Engine Misfire Detectionmentioning

confidence: 99%

Diagnosability performance analysis of models and fault detectors

Jung

2015

Linköping Studies in Science and Technology. Dissertations

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The cover: Graphical illustration of the proposed quantitative fault detectability and isolability performance measure, called distinguishability (Di,j(θ)). Typeset with L A T E X 2εPrinted by LiU-Tryck, Linköping, Sweden 2015 To Ylva i Abstract Model-based diagnosis compares observations from a system with predictions using a mathematical model to detect and isolate faulty components. Analyzing which faults that can be detected and isolated given the model gives useful information when designing a diagnosis system. This information can be used, for example, to determine which residual generators can be generated or to select a sufficient set of sensors that can be used to detect and isolate the faults. With more information about the system taken into consideration during such an analysis, more accurate estimations can be computed of how good fault detectability and isolability that can be achieved.Model uncertainties and measurement noise are the main reasons for reduced fault detection and isolation performance and can make it difficult to design a diagnosis system that fulfills given performance requirements. By taking information about different uncertainties into consideration early in the development process of a diagnosis system, it is possible to predict how good performance can be achieved by a diagnosis system and avoid bad design choices. This thesis deals with quantitative analysis of fault detectability and isolability performance when taking model uncertainties and measurement noise into consideration. The goal is to analyze fault detectability and isolability performance given a mathematical model of the monitored system before a diagnosis system is developed.A quantitative measure of fault detectability and isolability performance for a given model, called distinguishability, is proposed based on the Kullback-Leibler divergence. The distinguishability measure answers questions like "How difficult is it to isolate a fault f i from another fault f j ?". Different properties of the distinguishability measure are analyzed. It is shown for example, that for linear descriptor models with Gaussian noise, distinguishability gives an upper limit for the fault to noise ratio of any linear residual generator. The proposed measure is used for quantitative analysis of a nonlinear mean value model of gas flows in a heavy-duty diesel engine to analyze how fault diagnosability performance varies for different operating points. It is also used to formulate the sensor selection problem, i.e., to find a cheapest set of available sensors that should be used in a system to achieve required fault diagnosability performance.As a case study, quantitative fault diagnosability analysis is used during the design of an engine misfire detection algorithm based on the crankshaft angular velocity measured at the flywheel. Decisions during the development of the misfire detection algorithm are motivated using quantitative analysis of the misfire detectability performance showing, for example, varying detection performance a...

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“…Linear parametric classifiers were applied by [12] to diagnose misfire in internal combustion engines by using crank-angle domain digital filters to extract features from the measured engine speed signal, one of the characteristics of a misfire. A proper intelligent approach was utilized by [13][14][15] in a fault diagnosis of spark plugs in internal combustion engines based on acoustic and vibration signals through using sensor fusion and classifier combination. A knowledge-based approach as presented by [16,17] was used to develop an inspection process based on observations of the propagation of thermal waves.…”

Section: Introductionmentioning

confidence: 99%

Classification of crankshaft remanufacturing using Mahalanobis-Taguchi system

Abu

Jamaluddin

Zakaria

2022

Int. J. Automot. Mech. Eng.

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Remanufacturing is a process of returning a used product to at least its original performance with a warranty that is equivalent or better than that of a newly manufactured product. During a preliminary inspection on remanufacturing companies, it was found that there is no end life for crankshafts in terms of classifying it either to remanufacture, repair or reject due to limited information provided by the original equipment manufacturer. The manufacturer did not provide any information on the annual quantity produced and their specifications to the remanufacturing company for the purpose of referencing. Eventually, the distinctiveness of the remanufactured crankshaft from the original cannot be measured. Thus, the aim of this work is to classify crankshafts' end life into recovery operations based on the Mahalanobis-Taguchi System. The crankpin diameter of six engine models were measured in order to develop a scale that represents their population in a scatter diagram. It was found that on the diagram of each engine model, the left distributions from the center point belong to rejected crankshafts, the right distributions belong to re-manufacturable crankshafts, and the upper distributions belong to the repairable crankshafts. The developed scale is believed to be able to help remanufacturers instantaneously identify and match any unknown model crankshafts to its right category. The Ministry of International Trade & Industry (MITI) has established a remanufacturing policy under RMK11 and put in efforts to encourage Malaysians to venture into the remanufacturing business. Thus, this model will help the industry to understand and formulate their decision-making to sustain the end of life of their products.

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Residual generation and statistical pattern recognition for engine misfire diagnostics

Cited by 30 publications

References 17 publications

Development of misfire detection algorithm using quantitative FDI performance analysis

Development of misfire detection algorithm using quantitative FDI performance analysis

Diagnosability performance analysis of models and fault detectors

Classification of crankshaft remanufacturing using Mahalanobis-Taguchi system

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