Condition Monitoring Of Diesel Engines Research Articles

Conditional generative adversarial network based data augmentation for fault diagnosis of diesel engines applied with infrared thermography and deep convolutional neural network

This paper tries to introduce a new intelligent method for the early fault diagnosis of diesel engines. Firstly, infrared thermography (IRT) is introduced into diesel engine condition monitoring, then infrared images of diesel engines in four health states, such as normal condition, single-cylinder misfire, multi-cylinder misfire and air filter blockage, are collected and the region of interest (ROI) of infrared images are extracted. Next, conditional generative adversarial network (CGAN) is deployed to perform data augmentation on infrared image datasets. Then, deep convolutional neural network (DCNN) and Softmax regression (SR) classifier are used for automatically extracting infrared image fault features and pattern recognition, respectively. Finally, a comparison with three deep learning (DL) models is performed. The validation results show that the data augmentation method proposed in the paper can significantly improve the early fault diagnosis accuracy, and DCNN has the best fault diagnosis effect and resistance to temperature fluctuation interference among the four DL models.

Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer.

The reliability and safety of diesel engines gradually decrease with the increase in running time, leading to frequent failures. To address the problem that it is difficult for the traditional fault status identification methods to identify diesel engine faults accurately, a diesel engine fault status identification method based on synchro squeezing S-transform (SSST) and vision transformer (ViT) is proposed. This method can effectively combine the advantages of the SSST method in processing non-linear and non-smooth signals with the powerful image classification capability of ViT. The vibration signals reflecting the diesel engine status are collected by sensors. To solve the problems of low time-frequency resolution and weak energy aggregation in traditional signal time-frequency analysis methods, the SSST method is used to convert the vibration signals into two-dimensional time-frequency maps; the ViT model is used to extract time-frequency image features for training to achieve diesel engine status assessment. Pre-set fault experiments are carried out using the diesel engine condition monitoring experimental bench, and the proposed method is compared with three traditional methods, namely, ST-ViT, SSST-2DCNN and FFT spectrum-1DCNN. The experimental results show that the overall fault status identification accuracy in the public dataset and the actual laboratory data reaches 98.31% and 95.67%, respectively, providing a new idea for diesel engine fault status identification.

A Multivariate Statistics-Based Approach for Detecting Diesel Engine Faults with Weak Signatures

The problem of timely detecting the engine faults that make engine operating parameters exceed their control limits has been well-solved. However, in practice, a fault of a diesel engine can be present with weak signatures, with the parameters fluctuating within their control limits when the fault occurs. The weak signatures of engine faults bring considerable difficulties to the effective condition monitoring of diesel engines. In this paper, a multivariate statistics-based fault detection approach is proposed to monitor engine faults with weak signatures by taking the correlation of various parameters into consideration. This approach firstly uses principal component analysis (PCA) to project the engine observations into a principal component subspace (PCS) and a residual subspace (RS). Two statistics, i.e., Hotelling’s T 2 and Q statistics, are then introduced to detect deviations in the PCS and the RS, respectively. The Hotelling’s T 2 and Q statistics are constructed by taking the correlation of various parameters into consideration, so that faults with weak signatures can be effectively detected via these two statistics. In order to reasonably determine the control limits of the statistics, adaptive kernel density estimation (KDE) is utilized to estimate the probability density functions (PDFs) of Hotelling’s T 2 and Q statistics. The control limits are accordingly derived from the PDFs by giving a desired confidence level. The proposed approach is demonstrated by using a marine diesel engine. Experimental results show that the proposed approach can effectively detect engine faults with weak signatures.

The condition monitoring of diesel engines using acoustic signal analysis

<span>Engine have become one of the supportive asset for many activity and work, therefore engine need attention for its condition. The easiest way to do is through the acoustic sound of the engine itself. Most of the time, the engine sounds are checked using a traditional way that may causing a debate regarding its condition. This is due to no supportive scientific basis to know about engine condition using their own acoustic sound. A value from its frequency pattern is needed as scientific basis to determine an engine condition using acoustic sound. Method of envelope extraction by hilbert transform, fast fourier transform, and correlation coefficient are used to find that value. A series of tests have been carried out on the values that have been found and the result are promising for telling engine condition, but unfortunately the values has not been able to identify type of damage that occur on the engine.</span>

A new swarm intelligence optimized multiclass multi-kernel relevant vector machine: An experimental analysis in failure diagnostics of diesel engines

This work attempts to introduce a new intelligent method for condition monitoring of diesel engines. Diesel engine is one of the most important power providers for various industrial applications, including automobiles, ships, agricultures, construction, and electrical machinery. Due to harsh working environment, diesel engines are vulnerable to failures. This article addresses a significant need to improve predictive maintenance activities in diesel engines. A new failure diagnostics approach was proposed based on the manifold learning and swarm intelligence optimized multiclass multi-kernel relevant vector machine. Three manifold learning algorithms were first respectively used to fuse the features that extracted from the original vibration data of the diesel engines into a new nonlinear space. The fused features contain the most distinct health information of the engine by discarding redundant features. Then, the swarm intelligence optimized multiclass multi-kernel relevant vector machine was proposed to identify the failures using the fused features. The contribution of this research is that the dragonfly algorithm is employed to optimize the weights of the multi-kernel functions in the multiclass relevant vector machine. It was also applied to establishing a weighted-sum model by combining the outputs of swarm intelligence optimized multiclass multi-kernel relevant vector machine models with different manifold learning algorithms. Robust failure detection of diesel engines is achieved owing to combined strengths of multiple kernel functions and weighted-sum strategy. The effectiveness of the proposed method is demonstrated by experimental vibration data collected from a commercial diesel engine. The failure detection capability of the proposed manifold learning and swarm intelligence optimized multiclass multi-kernel relevant vector machine method for diesel engines will potentially benefit the machine condition monitoring industry by improving budgeting/forecasting and/or enabling just-in-time maintenance.

Independent Component Analysis and Comparative Analysis of Oil and Acoustic Emission Technique for Condition Monitoring of Diesel Engines

Engine is the heart of an automobile. The good conditions of components ensure the better performance of engine and the condition of components can be monitored through as follows, engine oil analysis, vibration analysis, infra red thermograph and non destructive tests. This paper discusses the possibility of wear or failure in the engine components by analyzing the engine oil and acoustic emission signals. As the engine oil is like life blood for engine, we can get rich data about the healthy condition by analyzing it like doctors analyzing our health through urine. And Analysis has done for various properties like viscosity, volatile matters, total base number, total acid number, ash content, flash point and wear metals particles like Iron, aluminum, copper, chromium, lead, and zinc in order to monitor the wear of the engine components. The properties of the oil will be changed based on the number of cycles over time period. The used oil can be collected from the engine and analyzed. And acoustic emission it is entirely different from traditional condition monitoring methods. It can give the condition of inner parts through the AE signals. The properties of engine oil as well as the AE signals are getting changes as the number of cycle and time increases.

Multisensor information integration for online wear condition monitoring of diesel engines

A diesel engine bench test was performed, and the online visual ferrograph (OLVF) and performance monitoring sensors were used to evaluate engine wear. The sliding window method was used to segment OLVF-monitoring data; features such as probability of smaller value and accumulated wear coefficient were extracted to clarify wear degree. The weighted combination multisensor information integration method was developed to calculate current engine condition factors. The results show that OLVF monitoring exhibits more sensitivity than other performance monitoring sensors. Using multisensor information provides an early warning of performance degradation ~40h before the diesel engine experiences a catastrophic fault.

An Approach on Fault Detection in Diesel Engine by Using Symmetrical Polar Coordinates and Image Recognition

Vibration technique provides useful information in fault detection of diesel engine, bringing significant cost benefits to diesel engine condition monitoring. Usually, time-frequency calculation on vibration signal is so complex that it is difficult to achieve online fault detection. In this paper, a method of fault detection in diesel engine is developed based on symmetrical polar coordinates and image recognition. In this method, time-domain waveform of vibration signal is transformed into snowflake-shaped in mirror symmetry pattern without time-frequency analysis. By the comparison of the geometric features of the snowflake images from different wear conditions of crankshaft bearing in diesel engines, we use centroid position and direction angle of the petal in snowflake image as features to detect the fault. Then, fuzzy c-means (FCM) are used to detect the conditions of the engine according to these features. In order to validate the methods, some experiments have been performed, the experimental results show that the centroid position and direction angle of the petal in snowflake image can reflect the information of different wear conditions in crankshaft bearing, and the fault of crankshaft bearing can be detected accurately. Hence, the method can work as fault detection in diesel engine, which is simple and effective, compared with time-frequency calculation method.

Application of Oil Analysis Techniques to Diesel Engine Condition Monitoring

In order to verify the validity of oil analysis for heavy diesel engine condition monitoring, the lubricating oil were sampled from the lubricating system of the domestic diesel engines, and then were tested by oil analysis (including contamination detection, periodic sampling test and ferrography technology). The results showed that oil analysis could monitor the lubricating oil contamination and mechanical wear condition to make diesel engines avoid early mechanical failure.

A New Intelligent Fusion Method of Multi-Dimensional Sensors and Its Application to Tribo-System Fault Diagnosis of Marine Diesel Engines

Marine diesel engines, a critical component to provide power for entire ships, have been received and still need considerable attentions to ensure their safety operation. Vibration and wear debris analysis are currently the most popular techniques for diesel engine condition monitoring and fault diagnosis. However, they are usually used independently in practice, and limited work has been done to address the integration of data collected using the two techniques. To enhance early fault detections, a new fault diagnosis technique for the marine diesel engine has been proposed by the information fusion of the vibration and wear particle analyses in this paper. A new independent component analysis with reference algorithm (ICA-R) using the empirical mode decomposition based reference extraction scheme was adopted to identify the characteristic source signals of the engine vibration collected from multi-channel sensors. The advantage of this approach performed at a data fusion level is that the ICA-R can extract only the relevant source directly related to the engine fault features in one separation cycle via incorporating prior knowledge. The statistical values of the recovered source signals were then calculated. The above vibration features, along with the wear particle characteristics, were used as the feature vectors for the engine fault detection. Lastly, the improved simplified fuzzy ARTMAP (SFAM) was applied to integrate the distinctive features extracted from the two techniques at a decision level to detect faults in a supervised learning manner. Particularly, the immune particle swarm optimization was used to tune the vigilance parameter of the SFAM to improve the identification performance. The experimental tests were implemented on a diesel engine set-up to evaluate the effectiveness of the proposed diagnosis approach. The diagnosis results have shown that distinguished fault features can be extracted and the fault identification accuracy is satisfactory. Moreover, the fault detection rate of the integration approach has been enhanced by 16.0 % or better when compared with using the two techniques separately.

Diesel engine condition monitoring using a multi-net neural network system with nonintrusive sensors

Abstract A multi-net fault diagnosis system, designed to provide power estimation and fault identification, is presented. Eight different faults: misfiring of each of the three cylinders, shaft imbalance, clogged intake and a leaking start plug for each cylinder were separately induced in the engine. Only the vibration and exhaust temperature sensors were used during the tests. The data obtained were applied to train a three-level multi-neural network system designed to estimate the load of the engine, its condition status (between failure and normal performance) and to identify the cause of the failure. Each level of the NN system uses different data to obtain the best individual and overall prediction accuracy. The results of this work prove that with the aid of neural networks, the load of a diesel engine and its condition can be stated based only on a few signals obtained from nonintrusive measurements, such as the exhaust temperature and surface vibration.

Development and application of a fully automatic troubleshooting method for large marine diesel engines

The diesel engine is the main propulsion system for marine vessels except for a small category using gas or steam turbines. This is the result of its high efficiency, power concentration and reliability that have been improved considerably during the current decade. Despite these advantages, the engineer usually has to overcome great difficulties and mainly operational problems arising during the engine's lifetime. In the case of large marine engines it is almost impossible to apply trial and error methods to solve engine operating problems. This is amplified by the fact that almost all large marine diesel engines are turbocharged ones making the problem even more severe because of the interaction between the engine and the exhaust gas turbocharger. For this reason various diagnosis methods have been proposed for diesel engine condition monitoring that are mainly statistical based on known engine operating curves. These systems provide general information only and do not reveal the actual cause for an engine fault or low performance. In the current work an advanced automatic troubleshooting method based mainly on thermodynamics is presented to monitor the engine condition and to detect the actual cause for an engine fault. The method is based on the processing of measured engine data using a simulation model and provides the current engine condition and its tuning. An application of the method on a marine vessel powered by a slow speed two stroke marine diesel engine suffering from high cylinder exhaust gas temperatures and low power output is given in the present work. The method is applied at sea under actual engine operating conditions. From the processing of measured data the diagnosis method provides the current engine condition and the cause for the low power output from which the engine suffered. After conducting the major repair/adjustments proposed by the diagnosis method a substantial improvement in engine behavior was observed providing a validation for the proposed method.

Ferrography applied to diesel engine oil analysis

Ferrography, developed over 10 years ago, has to date had restricted usage in the field of condition monitoring of diesel engines. A number of applications are illustrated where this technique has now been effectively utilized and reasons are also suggested for its restricted use in comparison with other existing techniques such as spectrographic oil analysis. Cases are also described where ferrography used in conjunction with spectroscopy has provided a powerful technique in the diesel engine condition monitoring field.

Laboratory engine testing of marine diesel cylinder lubricants : (A lecture) A. J. S. Baker and R. T. Davies. For a short version see Sci. Lubrication, 15 (1963) 19

The diesel engine is the main propulsion system for marine vessels except for a small category using gas or steam turbines. This is the result of its high efficiency, power concentration and reliability that have been improved considerably during the current decade. Despite these advantages, the engineer usually has to overcome great difficulties and mainly operational problems arising during the engine's lifetime. In the case of large marine engines it is almost impossible to apply trial and error methods to solve engine operating problems. This is amplified by the fact that almost all large marine diesel engines are turbocharged ones making the problem even more severe because of the interaction between the engine and the exhaust gas turbocharger. For this reason various diagnosis methods have been proposed for diesel engine condition monitoring that are mainly statistical based on known engine operating curves. These systems provide general information only and do not reveal the actual cause for an engine fault or low performance. In the current work an advanced automatic troubleshooting method based mainly on thermodynamics is presented to monitor the engine condition and to detect the actual cause for an engine fault. The method is based on the processing of measured engine data using a simulation model and provides the current engine condition and its tuning. An application of the method on a marine vessel powered by a slow speed two stroke marine diesel engine suffering from high cylinder exhaust gas temperatures and low power output is given in the present work. The method is applied at sea under actual engine operating conditions. From the processing of measured data the diagnosis method provides the current engine condition and the cause for the low power output from which the engine suffered. After conducting the major repair/adjustments proposed by the diagnosis method a substantial improvement in engine behavior was observed providing a validation for the proposed method.