Detection and Diagnosis of Incipient Faults in Heavy-Duty Diesel Engines

Morgan, I.L.; Liu, Honghai; Tormos, Bernardo; Sala, Antonio

doi:10.1109/tie.2009.2038337

Cited by 40 publications

(14 citation statements)

References 25 publications

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“…Examples can be found in the aerospace [26], defense [38], power systems [39], water treatment [35], and buildings industries [36,40,41]. Among articles that use di↵erent categories of fault definition for di↵erent faults, condition-based definitions are also common, for example, Morgan et al [42].…”

Section: Condition-basedmentioning

confidence: 99%

“…A few articles describe mixes of faults, of which some have a behavior-based ground-truth definition: diesel engine overheating [42], reduced condenser and evaporator water flow rates for chillers [31], and failure to maintain air handling unit temperature and pressure set points [37]. Regardless of the ground truth definition, use of equipment behavior as the primary fault detection criteria is common in FDD algorithms, particularly rule-based algorithms that leverage indirect sensor readings [24,25,36,45].…”

Section: Behavior-basedmentioning

confidence: 99%

“…condition. This type of input sample is well-suited for use with continuous processes and has been used in diverse contexts, including for aerospace applications [27], diesel engines [42], wastewater treatment [35], chillers [22], and air conditioning equipment [25,40].…”

Section: Definition Of An Input Samplementioning

confidence: 99%

See 2 more Smart Citations

A performance evaluation framework for building fault detection and diagnosis algorithms

Frank¹,

Lin²,

Jin³

et al. 2019

Energy and Buildings

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Fault detection and diagnosis (FDD) algorithms for building systems and equipment represent one of the most active areas of research and commercial product development in the buildings industry. However, far more e↵ort has gone into developing these algorithms than into assessing their performance. As a result, considerable uncertainties remain regarding the accuracy and e↵ectiveness of both research-grade FDD algorithms and commercial products-a state of a↵airs that has hindered the broad adoption of FDD tools. This article presents a general, systematic framework for evaluating the performance of FDD algorithms. The article focuses on understanding the possible answers to two key questions: in the context of FDD algorithm evaluation, what defines a fault and what defines an evaluation input sample? The answers to these questions, together with appropriate performance metrics, may be used to fully specify evaluation procedures for FDD algorithms.

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Section: Condition-basedmentioning

confidence: 99%

Section: Behavior-basedmentioning

confidence: 99%

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A performance evaluation framework for building fault detection and diagnosis algorithms

Frank¹,

Lin²,

Jin³

et al. 2019

Energy and Buildings

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show abstract

“…A number of studies on feature extraction and classification algorithms are reported in the literature [5][6][7][8][9][10][11][12][13][14]. A number of studies on feature extraction and classification algorithms are reported in the literature [5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Incipient fault detection methods in distribution system have become an interesting topic for researches and scholars. A number of studies on feature extraction and classification algorithms are reported in the literature [5][6][7][8][9][10][11][12][13][14]. Among them, harmonic analysis, randomness detection, artificial neural networks, Hilbert-transform-based, wavelet transform, et al are used to extract the feature of incipient fault signals.…”

Section: Introductionmentioning

confidence: 99%

Incipient fault classification using a combination of S-transform and Hilbert transform method

Zhai

Chen

et al. 2012

Int. Trans. Electr. Energ. Syst.

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SUMMARY Incipient faults such as those caused by tree contacts are difficult to detect by conventional relaying algorithms. These faults impact distribution feeder reliability by advancing –over time– into a full‐blown fault if not detected in time. This paper proposes a combined technique using S‐transform technique and Hilbert transform to extract the features of incipient fault. A least square support vector machine classifier is developed based on the features so that incipient faults can be distinguished from the normal disturbances. The comparison of the results of different feature selection methods has been conducted. The simulation results show that the proposed feature selection method is feasible and suitable. Copyright © 2012 John Wiley & Sons, Ltd.

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Simultaneous-fault detection based on qualitative symptom descriptions for automotive engine diagnosis

Vong

Wong

2014

Applied Soft Computing

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Detection and Diagnosis of Incipient Faults in Heavy-Duty Diesel Engines

Cited by 40 publications

References 25 publications

A performance evaluation framework for building fault detection and diagnosis algorithms

A performance evaluation framework for building fault detection and diagnosis algorithms

Incipient fault classification using a combination of S-transform and Hilbert transform method

Simultaneous-fault detection based on qualitative symptom descriptions for automotive engine diagnosis

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