Condition monitoring technologies, parameters and data processing techniques for fault detection of internal combustion engines: A literature review

Bingamil, Ahmed Ameen; Alsyouf, Imad; Cheaitou, Ali

doi:10.1109/icecta.2017.8252038

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…The application of CM extends beyond traditional high-value equipment in various industries as advancements in sensor technology and data processing have democratized its adoption across diverse industrial sectors. This paradigm shift is particularly evident in industries like electrified automotive drivelines [3] and railway applications [4]. However, the realization of CM's whole potential is based on identifying reliable condition indicators (CIs) that are capable of accurately discerning incipient failures [5].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Condition Monitoring Algorithm for Early Bearing Fault Detection

Gruber,

Fuchs,

Bader

2024

Sensors

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Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without creating false positive failure states. Therefore, this paper introduces a novel algorithm for the early detection of roller bearing failures, particularly tailored to high-precision bearings and automotive test bed systems. The featured method (AFI—Advanced Failure Indicator) utilizes the Fast Fourier Transform (FFT) of wideband accelerometers to calculate the spectral content of vibration signals emitted by roller bearings. By calculating the frequency bands and tracking the movement of these bands within the spectra, the method provides an indicator of the machinery’s health, mainly focusing on the early stages of bearing failure. The calculated channel can be used as a trend indicator, enabling the method to identify subtle deviations associated with impending failures. The AFI algorithm incorporates a non-static limit through moving average calculations and volatility analysis methods to determine critical changes in the signal. This thresholding mechanism ensures the algorithm’s responsiveness to variations in operating conditions and environmental factors, contributing to its robustness in diverse industrial settings. Further refinement was achieved through an outlier detection filter, which reduces false positives and enhances the algorithm’s accuracy in identifying genuine deviations from the normal operational state. To benchmark the developed algorithm, it was compared with three industry-standard algorithms: VRMS calculations per ISO 10813-3, Mean Absolute Value of Extremums (MAVE), and Envelope Frequency Band (EFB). This comparative analysis aimed to evaluate the efficacy of the novel algorithm against the established methods in the field, providing valuable insights into its potential advantages and limitations. In summary, this paper presents an innovative algorithm for the early detection of roller bearing failures, leveraging FFT-based spectral analysis, trend monitoring, adaptive thresholding, and outlier detection. Its ability to confirm the first failure state underscores the algorithm’s effectiveness.

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Section: Introductionmentioning

confidence: 99%

Evaluation of a Condition Monitoring Algorithm for Early Bearing Fault Detection

Gruber,

Fuchs,

Bader

2024

Sensors

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“…As the most important machine for automotive applications, many advanced sensors, such as accelerometer, speed, torque, temperature, and pressure sensors have been installed on the IC engine to collect the original data and then obtain the machine's performance [8,14,15]. Nowadays, the online condition monitoring techniques for IC engines include measuring temperature, pressure, rotation, oxygen, airflow, and utilizing methods of acoustic emission, vibration, oil and fuel analysis [16]. This results in the collection of large amount of data and monitoring challenges as the data for more than one machine cannot be all recorded and analyzed in timely manner by experts.…”

Section: Introductionmentioning

confidence: 99%

“…This results in the collection of large amount of data and monitoring challenges as the data for more than one machine cannot be all recorded and analyzed in timely manner by experts. Therefore, performance assessment frameworks have been proposed for making the effective measure indicators which could be calculated and represent the state of the engine [16,17]. However, the current assessment approaches have been proposed for the engines which are powered by common fuel, such as gasoline and diesel, and they were designed to evaluate the different engine states under the constant fuel type.…”

Section: Introductionmentioning

confidence: 99%

Online Big-Data Monitoring and Assessment Framework for Internal Combustion Engine with Various Biofuels

Zhang

Sharma

Wang

et al. 2023

IJAMM

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Article Online Big-Data Monitoring and Assessment Framework for Internal Combustion Engine with Various Biofuels Ming Zhang 1,*, Vikas Sharma 2, Zezhong Wang 1, Yu Jia 1, Abul Kalam Hossain 1, and Yuchun Xu 1 1 College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK 2 School of Architecture, Technology and Engineering, University of Brighton, Brighton BN2 4GJ, UK * Correspondence: m.zhang21@aston.ac.uk Received: 14 December 2022 Accepted: 26 April 2023 Published: 30 May 2023 Abstract: As the primary power source for automobiles, the internal combustion (IC) engines have been widely used and served millions of people worldwide. With increasingly stringent environmental regulations, biofuels have been obtained more attentions and are being used as alternative fuel to power IC engines. However, there are currently no standard solutions or well-established monitoring and assessment methods that can effectively evaluate the IC engine’s performance with biofuels. The expectation for biofuels is to keep the engine’s lifetime as long as the conventional fuels, or even longer. Otherwise, their usage would be unnecessary because they would reduce the lifecycle of the engine and also cause more waste and pollution. To address this challenge, we initially designed two biofuels: waste cooking oil biofuel (WCOB) and lamb fat biofuel (LFB). Then we proposed an online big-data monitoring and assessment framework for IC engines operating with various types of fuel. We conducted comprehensive experiments and comparisons based on the proposed framework. The results indicate that LFB performs best under all the performance indicators.

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“…An overworked engine without planned maintenance can lead to engine failure, and it is also likely to end up with terrible loss [15]- [17]. When an engine component does not perform smoothly, diagnostic activities should be solved to discover the possible cause.…”

Section: Introductionmentioning

confidence: 99%

Spark plug failure detection using Z-freq and machine learning

et al. 2021

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Preprogrammed monitoring of engine failure due to spark plug misfire can be traced using a method called machine learning. Unluckily, a challenge to get a high-efficiency rate because of a massive volume of training data is required. During the study, these failure-generated were enhanced with a novel statistical signal-based analysis called Z-freq to improve the exploration. This study is an exploration of the time and frequency content attained from the engine after it goes under a specific situation. Throughout the trial, the misfire was formed by cutting the voltage supplied to simulate the actual outcome of the worn-out spark plug. The failure produced by fault signals from the spark plug misfire were collected using great sensitivity, space-saving and a robust piezo-based sensor named accelerometer. The achieved result and analysis indicated a significant pattern in the coefficient value and scattering of Z-freq data for spark plug misfire. Lastly, the simulation and experimental output were proved and endorsed in a series of performance metrics tests using accuracy, sensitivity, and specificity for prediction purposes. Finally, it confirmed that the proposed technique capably to make a diagnosis: fault detection, fault localization, and fault severity classification.

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Condition monitoring technologies, parameters and data processing techniques for fault detection of internal combustion engines: A literature review

Cited by 11 publications

References 33 publications

Evaluation of a Condition Monitoring Algorithm for Early Bearing Fault Detection

Evaluation of a Condition Monitoring Algorithm for Early Bearing Fault Detection

Online Big-Data Monitoring and Assessment Framework for Internal Combustion Engine with Various Biofuels

Spark plug failure detection using Z-freq and machine learning

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