Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer

Wang, Yingmin; Zhang, Fujun; Cui, Tao; Zhou, Jinlong

doi:10.3901/cjme.2015.1211.145

Cited by 25 publications

(8 citation statements)

References 23 publications

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“…Therefore, engineers are doing continuous researches to find optimal fault detection system for diesel engine in order to monitor the health condition of the engine. Several types of signals can be measured for monitoring purpose including rotational angle speed [1], temperature [2], fuel and oil quality [3] [4], and vibration [5] [6] [7].…”

Section: Introductionmentioning

confidence: 99%

Diesel Engine Fault Diagnosis with Vibration Signal

Tharanga¹,

Liu²,

Zhang³

et al. 2020

JAMP

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When the vibration of diesel engine structure is measured, the signal is composed of a very complex superposition of the contributions of different vibratory sources modified by their respective transmission paths. These sources originate from several internal phenomenons in the engine such as combustion pressure variation, unbalanced reciprocating and rotating parts. In a diesel engine, movement parts work in a specific order. Once the starting point is determined, occurrence of work order in different cycle phases can be determined. This could successfully use to identifying of impulses in complex vibration signal of a diesel engine. From the variation of features of those impulses, it is possible to determine the working condition of the engine. This can use to fault diagnosis of diesel engine, specially faults related to combustion process.

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Section: Introductionmentioning

confidence: 99%

Diesel Engine Fault Diagnosis with Vibration Signal

Tharanga¹,

Liu²,

Zhang³

et al. 2020

JAMP

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“…As for the production line machine, the most vital component is the motor providing power. In practice, motor faults are most likely to have serious consequences, so that most of the current studies focus on motor fault detection [1], [10], [11], [33]. However, most of the current studies rely on professional knowledge such as high-level signal processing [9] and fault identification, and input data are entered into the classification model after feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Motor Fault Detection and Feature Extraction Using RNN-Based Variational Autoencoder

2019

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In most of the fault detection methods, the time domain signals collected from the mechanical equipment usually need to be transformed into frequency domain or other high-level data, highly relying on professional knowledge such as signal processing and fault pattern recognition. Contrary to those existing approaches, we proposed a two-stage machine learning analysis architecture which can accurately predict the motor fault modes only by using motor vibration time-domain signals without any complicated preprocessing. In the first stage, the method RNN-based VAE was proposed which is highly suitable for dimension reduction of time series data. In addition to reducing the dimension of sequential data from 150 * 3 to 25 dimensions, our method furthermore improves the prediction accuracy evaluated by several classification algorithms. While other dimension reduction methods such as Autoencoder and Variational Autoencoder cannot improve the classification accuracy effectively or even decreased. It indicates that the sequential data after dimension reduction via the RNN-based VAE still can maintain the high-dimensional data information. Furthermore, the experimental results demonstrate that it can be well applied to time series data dimension reduction and shows a significant improvement of the prediction performance, even with a simple double-layer Neural Network can reach over 99% of accuracy. In the second stage, Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are used to further perform the second dimension reduction, such that the different or unknown fault modes can be clearly visualized and detected. INDEX TERMS Motor fault detection, feature extraction, recurrent neural network, variational autoencoder.

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“…Several types of signals can be measured for monitoring purposes including pressure, 2 rotational angle speed, 3 temperature, 4 fuel and oil quality, [5][6][7] and vibration. [8][9][10] Vibration monitoring is a reliable method for detecting machine abnormality, considering the fact that different faults especially those related to combustion are the source of vibration.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis of a 12-cylinder trainset diesel engine based on vibration signature analysis and neural network

Zabihihesari

Ansari-Rad

Shirazi

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper presents a condition monitoring and combustion fault detection technique for a 12-cylinder 588 kW trainset diesel engine based on vibration signature analysis using fast Fourier transform, discrete wavelet transform, and artificial neural network. Most of the conventional fault diagnosis techniques in diesel engines are mainly based on analyzing the difference of vibration signals amplitude in the time domain or frequency spectrum. Unfortunately, for complex engines, the time- or frequency-domain approaches do not provide appropriate features solely. In the present study, vibration signals are captured from both intake manifold and cylinder heads of the engine and were analyzed in time-, frequency-, and time–frequency domains. In addition, experimental data of a 12-cylinder 588 kW diesel engine (of a trainset) are captured and the proposed method is verified via these data. Results show that power spectra of vibration signals in the low-frequency range reliably distinguish between normal and faulty conditions. However, they cannot identify the fault location. Hence, a feature extraction method based on discrete wavelet transform and energy spectrum is proposed. The extracted features from discrete wavelet transform are used as inputs in a neural network for classification purposes according to the location of sensors and faults. The experimental results verified that vibration signals acquired from intake manifold have more potential in fault detection. In addition, the capacity of discrete wavelet transform and artificial neural network in detection and diagnosis of faulty cylinders subjected to the abnormal fuel injection was revealed in a complex diesel engine. Beside condition monitoring of the engine, a two-step fault detection method is proposed, which is more reliable than other one-step methods for complex engines. The average condition monitoring performance is from 93.89% up to 99.17%, based on fault location and sensor placement, and the minimum classification performance is 98.34%.

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Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer

Cited by 25 publications

References 23 publications

Diesel Engine Fault Diagnosis with Vibration Signal

Diesel Engine Fault Diagnosis with Vibration Signal

Motor Fault Detection and Feature Extraction Using RNN-Based Variational Autoencoder

Fault detection and diagnosis of a 12-cylinder trainset diesel engine based on vibration signature analysis and neural network

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