Failure analysis of fuel pumps used for diesel engines in transport utility vehicles

Deulgaonkar, Vikas Radhakrishna; Pawar, Kundan; Kudle, Pratik; Raverkar, Atharva; Raut, Amod

doi:10.1016/j.engfailanal.2019.07.048

Cited by 24 publications

(7 citation statements)

References 5 publications

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“…Research studies on vibration-based FDI of ICPS equipment have been on the rise, including, but not limited to, pumps, bearings, gears, etc. These have motivated several manufacturers to integrating on-board accelerometers into their products but the challenge of utilizing the complex nonstationary signals from these sensors remains a problem facing optimum real-time condition monitoring and predictive maintenance of ICPS equipment/components [13]. The past few decades recorded several innovations on advanced signal processing for time domain, frequency domain, and the more robust TFD diagnostic feature extraction with reliable successes; however, because each technique has its unique pros and cons for dynamic modeling alongside significant assumptions/tradeoffs during dynamic modeling, their reliability for safety-critical systems are still being questioned [14,15].…”

Section: Related Workmentioning

confidence: 99%

D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

Akpudo

Hur

2021

Energies

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This paper develops a novel hybrid feature learner and classifier for vibration-based fault detection and isolation (FDI) of industrial apartments. The trained model extracts high-level discriminative features from vibration signals and predicts equipment state. Against the limitations of traditional machine learning (ML)-based classifiers, the convolutional neural network (CNN) and deep neural network (DNN) are not only superior for real-time applications, but they also come with other benefits including ease-of-use, automated feature learning, and higher predictive accuracies. This study proposes a hybrid DNN and one-dimensional CNN diagnostics model (D-dCNN) which automatically extracts high-level discriminative features from vibration signals for FDI. Via Softmax averaging at the output layer, the model mitigates the limitations of the standalone classifiers. A diagnostic case study demonstrates the efficiency of the model with a significant accuracy of 92% (F1 score) and extensive comparative empirical validations.

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Section: Related Workmentioning

confidence: 99%

D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

Akpudo

Hur

2021

Energies

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“…An effective onboard management system can minimize and significantly reduce the risk of engine break-down caused by the failure of FIP elements due to jammed/sticking fuel/VIT racks and/or a seized B-P precision pair if it is implemented and followed at all times. Proactive countermeasures mentioned in this article can help to avoid operational problems with the FIP and to minimize delays in commercial operations and additional costs due to the unavailability of M/E, but cannot solve them completely, especially due to the limitation of establishing the prioritization of FIP overhauling as per the maintenance schedule [20][21][22][23]. The unit of PSD is energy per frequency (width), and it obtains energy within a specific frequency range by integrating PSD within that frequency range.…”

Section: Discussionmentioning

confidence: 99%

The Use of Acoustic Emission to Diagnosis of Fuel Injection Pumps of Marine Diesel Engines

Bejger

Drzewieniecki

2019

Energies

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The article draws attention to the problems of maintaining fuel injection pumps of marine diesel engines in the conditions of the use of residual fuels in which the quality is steadily deteriorating. The analysis of tribological processes occurring in hydraulic precision pairs of fuel injection pumps, such as a barrel-plunger, is presented. Problems occurring regarding the operation of injection pumps and the possibilities of their avoidance on board are discussed. The means of condition monitoring, including the application of thermography methods, are characterized. The authors have done research concerning diagnosing injection systems of high and medium power engines by using acoustic emission (AE) signals. The experiment results obtained with the use of acoustic emission and wavelet analysis confirmed the dependency of the frequency components contained in the acoustic emission signal on the condition state of the injector pumps’ tribological pair.

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“…The pumps were operated under the recorded conditions for 10 days (8 h per day) while being monitored. These recorded working conditions were chosen due to recent findings on these conditions being the most probable failure modes these pumps (and solenoid valves) are prone to [6].…”

Section: Methodsmentioning

confidence: 99%

“…Recently, vibration monitoring has become a highly reliable condition monitoring paradigm for fluid pumps (and most systems) [11]; consequently, pump manufacturers are recently integrating on-board vibration (and temperature) sensors into their products; however, interpreting and drawing accurate conclusions from the complex big data has been one of the setbacks facing optimized fluid pump FDI and prognostics [6]. As a result that raw vibrational signals are non-stationary with background noise, extracting salient features from these signals remains an on-going challenge for accurately understanding the underlying dynamics of targeted systems.…”

Section: Motivation Related Work and Major Contributionsmentioning

confidence: 99%

“…For this sole purpose, they are usually operated for as long as required of them; thereby exposing them to diverse failure modes from sources ranging from unfavorable environmental conditions, fatigue, fluid contamination, mechanical/electrical stress, uncertainty, filter failure/clogging, overuse/underuse, etc. [6]. Consequently, the need for real-time health monitoring (and assessment) becomes crucial for accurate diagnosis, reliable prognosis and more profitable decision making.…”

Section: Introductionmentioning

confidence: 99%

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A Cost-Efficient MFCC-Based Fault Detection and Isolation Technology for Electromagnetic Pumps

Akpudo

Hur

2021

Electronics

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Fluid pumps serve critical purposes in hydraulic systems so their failure affects productivity, profitability, safety, etc. The need for proper condition monitoring and health assessment of these pumps cannot be overemphasized and this has resulted in extensive research studies on standard techniques for ensuring optimum fault detection and isolation (FDI) results for these pumps. Interestingly, mechanical vibrational signals reflect operating conditions and by exploring the robust time–frequency-domain feature extraction techniques, the underlying nonlinear characteristics can be captured for reliable fault diagnosis/condition assessment. This study is based on the use of vibrational signals for fault isolation of electromagnetic pumps. From the vibrational signals, Mel frequency cepstral coefficients (MFCCs), the first-order and the second-order differentials were extracted and the salient features selected by a rank-based recursive feature elimination (RFE) of uncorrelated features. The proposed framework was tested and validated on five VSC63A5 electromagnetic pumps at various fault conditions and isolated/classified using the Gaussian kernel SVM (SVM-RBF-RFE). Results show that the proposed feature selection approach is computationally cheaper and significantly improves diagnostics performance. In addition, the proposed framework yields a comparatively better diagnostics results on electromagnetic pumps in comparison with other diagnostics methods, hence a more reliable diagnostics tool for electromagnetic pumps.

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Failure analysis of fuel pumps used for diesel engines in transport utility vehicles

Cited by 24 publications

References 5 publications

D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

The Use of Acoustic Emission to Diagnosis of Fuel Injection Pumps of Marine Diesel Engines

A Cost-Efficient MFCC-Based Fault Detection and Isolation Technology for Electromagnetic Pumps

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