Research on performance evaluation of production vehicle based on multi-objective genetic algorithm

Aimed at the fault diagnosis and prediction of automobile engine, firstly designed a framework structure of automobile engine fault diagnosis and prediction system, and built a hardware platform; Secondly adopted the genetic algorithm neural network to fault prediction and diagnosis reasoning; Finally after analyzing automobile exhaust components, engine vibration, engine abnormal sound parameters, inferred the appeared and impending fault of automobile then made the tips for users on the screen. The results show that the performance of system is well, the accuracy of diagnosis and prediction is 95% in different conditions of experiment and debugging.

This paper searches mixed model two-sided assembly line type I balancing problem based on Y automobile manufacturing company. According to the characteristics and balance constraints of mixed model two-sided assembly line, the actual problem is abstracted as the mathematical model. The three minimal objectives are the station number, the balance index and the wait time. In this study, an advanced multi-objective genetic algorithm is proposed for analyzing and solving the satisfactory solution set of multi-objective optimization problem and example application shows its validity.

Abstract. In this paper we have used a combination of three algorithms: Dynamic time warping (DTW) and the coefficients of Mel frequency Cepstrum (MFC) and Fast Fourier Transformation (FFT) for classifying various engine faults. Dynamic time warping and MFCC (Mel Frequency Cepstral Coefficients), FFT are used usually for automatic speech recognition purposes. This paper introduces DTW algorithm and the coefficients extracted from Mel Frequency Cepstrum, FFT for automatic fault detection and identification (FDI) of internal combustion engines for the first time. The objective of the current work was to develop a new intelligent system that should be able to predict the possible fault in a running engine at different-different workshops. We are doing this first time. Basically we took different-different samples of Engine fault and applied these algorithms, extracted features from it and used Fuzzy Rule Base approach for fault Classification.

A multiobjective genetic algorithm (MOGA) is applied to a test batch scheduling problem to optimize five objectives simultaneously. The design of the MOGA allows an emphasis on human interaction with the optimization process, including the ability to change priorities of preferences and plant data interactively, and to allow the MOGA to make decisions regarding batch size and the rule task allocation. Experimental results demonstrate the development of this technique, allowing the combination of human expertise and MOGA optimization power to provide scheduling solutions to a highly complex problem.

In order to meet the requirements of multi-parameter and real-time fault diagnosis of China VI vehicle emission standards diesel engine air system, this paper focuses on the research of the diesel engine air system fault detection and diagnosis method based on deep learning to improve the operational safety and fault diagnosis efficiency. In this paper, the air system fault detection and diagnosis are completed based on the AE model and the CNN model, combined with the actual operation of the diesel engine. Among them, the AE model successfully detected all real-time operation faults, and the false detection rate on the health data was 0.1162%; the CNN model obtained a 90.77% fault diagnosis accuracy rate on the test set of the real-time operation data set. The results show that the model has high accuracy in the diagnosis of diesel engine faults, which is of great significance to the application of deep learning based on big data processing.

The battery system, as the core energy storage device of new energy vehicles, faces increasing safety issues and threats. An accurate and robust fault diagnosis technique is crucial to guarantee the safe, reliable, and robust operation of lithium-ion batteries. However, in battery systems, various faults are difficult to diagnose and isolate due to their similar features and internal coupling relationships. In this paper, the current research of advanced battery system fault diagnosis technology is reviewed. Firstly, the existing types of battery faults are introduced in detail, where cell faults include progressive and sudden faults, and system faults include a sensor, management system, and connection component faults. Then, the fault mechanisms are described, including overcharge, overdischarge, overheat, overcool, large rate charge and discharge, and inconsistency. The existing fault diagnosis methods are divided into four main types. The current research and development of model-based, data-driven, knowledge-based, and statistical analysis-based methods for fault diagnosis are summarized. Finally, the future development trend of battery fault diagnosis technology is prospected. This paper provides a comprehensive insight into the fault and defect diagnosis of lithium-ion batteries for electric vehicles, aiming to promote the further development of new energy vehicles.

With the rapid advancement of industrial technology and the continuous expansion of production scale, electromechanical special equipment (ESE), such as cranes, has become increasingly essential in industrial operations. The demands for safety, reliability, and operational efficiency of this equipment have grown significantly. Extensive research has been conducted on fault diagnosis (FD) technologies for ESE to enhance their operational lifespan and ensure personnel safety. However, despite the considerable body of research, a comprehensive and systematic review in this field remains conspicuously absent. Therefore, this paper summarizes and analyzes the FD technologies associated with ESE, detailing the principal processes and the current status of technology applications. The typical failure modes that can compromise the performance are analyzed by thoroughly examining the core components of such equipment. Moreover, an in-depth discussion on FD methodologies is provided from the perspectives of traditional methods, signal processing methods, and machine learning methods. By examining these methods side by side, their strengths, limitations, and suitability for different types of ESE are highlighted. Finally, this paper offers a forward-looking assessment of the future development trajectory for FD technologies for ESE. This paper provides a comprehensive review and outlines a future development path on FD techniques in ESE, which will contribute to the continued development of these techniques.

With the increasing performance requirements of advanced fighters, the engine structure is becoming more complex, which makes the traditional fault diagnosis technologies have more difficulties to meet these new requirements. Based on the requirements of rapid, accurate and continuous support, the engine health management technology has been applied in the engineering area. In this paper, we analyzed the related results and applications of engine condition monitoring and fault diagnosis system, studied and designed the architecture of engine health management system, analyzed the key technologies in detail, presented the workflow, and get the conclusion of the influences on engine maintenance support mode. The research is helpful for the engine health management system design, which is the base of engine maintenance support system optimization, and is the reference for improving engine fault diagnosis level and maintenance capability.

Background: The idea behind the Internet of Things is to bring the virtual world into the physical one by connecting commonplace items. With the help of the Internet of Things (IoT), it is possible to remotely sense or control objects through preexisting network infrastructure. This opens up possibilities for computer-based systems to integrate with the physical world, which in turn improves efficiency, accuracy, and economic benefit while reducing the need for human intervention. Objective: The purpose of this patent study is to investigate how a (NSGA-II) multi-objective genetic algorithm might be utilized to optimize the execution of an Internet of Things (IoT) temperature monitoring Box-Type Solar Cooker (BTSC). To determine the best set of output parameters for an IoT temperature monitoring box-type solar cooker, (NSGA-II) multi-objective genetic algorithms are used to perform optimizations of the figure of merits (F2), cooking power, cooker efficiency, and final water temperature. Methods: The present research work involves the development of a Wi-Fi module system integrated with a smart temperature monitoring system for a BTSC. Keeping track of the temperature data from different locations in the BTSC through the IoT system was the primary objective of this project. A waterproof temperature sensor (DS18B20) was used to keep monitoring. After that, the data was shown on an LCD, stored on a microSD card, and made available through a smartphone. The Blynk Applications' IoT was employed. Using existing data, regression-based computational models are developed to describe the complex correlations between the decision-processing parameters and the input parameters of an IOT-based solar cooker. These models are applied in the objective functions after determining that a genetic algorithm is more appropriate for the problem. To forecast the optimal values about the figure of merits (F2), cooking power, cooker efficiency, and final water temperature, the Pareto fronts have been developed. Results: We compare the values of response variables that were gathered experimentally with the values that were predicted by NSGA-II. The predicted values are found to be quite close to experimental values. This indicates that the multi-objective optimization method, as used in this study, has very good prediction performance. The test results are graphically shown using the error bar. Therefore, it is clear that the optimization process used to adjust the parameters of the solar cooker's performance has been quite effective. According to the findings of the experiment, the temperature at which a cooking pot remained stagnant on average was 158°C. It was determined that the cooker was of class A based on the values of the first figure of merit (F1), the second figure of merit (F2), and the cooking power (P), which were respectively 0.132, 0.359, and 86.108 W. Therefore, the thermal efficiency of the IoT-base temperature monitoring box type solar cooker is 39.99 %. Conclusion: The findings of this inquiry furthermore produced the outcome that the model provided can be applied conveniently with a confidence level of 95% to calculate the figure of merits (F2), cooking power, cooker efficiency, and final water temperature value of an Internet of Things-based temperature monitoring BTSC. The performance of IoT-based BTSC is optimized by providing real- time monitoring and data visualization, ultimately improving their efficiency and reliability. This research provides an educational tool to promote awareness and understanding of renewable energy sources and their potential benefits.

Fault diagnosis method based on incremental enhanced supervised locally linear embedding and adaptive nearest neighbor classifier

Application of extension theory in misfire fault diagnosis of gasoline engines

Accurate aircraft engine fault detection and diagnosis is vitally important reducing operating costs and improving safety. Various data and knowledge could be collected from manufacture, test bed measurement systems, on-board measurement systems, maintenance history and experts' experience. Integrating and fusing these data and information to provide intelligent fault diagnosis and maintenance schedules are essentially to both civil and military engines. Information fusion strategies and architectures have been developed over the last several years for improving upon the accuracy, robustness and overall effectiveness of diagnostic and prognostic technologies. Fusion of relevant sensor data, maintenance database information, and various diagnostic and prognostic technologies has been proven effective in reducing false alarm rates, increasing confidence levels in early fault detection, and predicting time to failure or degraded condition requiring maintenance action. In this paper, we researched on the architectures of fusion systems. A four-level model was presented to fit the usage in aircraft engines fault detection and diagnosis. To generate the deviations for gas path fault detection, baseline values should be chosen firstly. We analyzed choosing baseline values and generating deviations in detail. Then, the fuzzy set was introduced to descript the degree of symptoms belonging to fault patterns. A method based on fuzzy set to isolate fault and quantify the deterioration of performance of components was presented. We also demonstrated the method with an example. For this method, it is not necessary to have more measurements than fault patterns. The fault diagnosis system based on it is very easy to construct and extend too

With the development of the Industrial Internet of Things (IIoT) technology, fault diagnosis has emerged as a critical component of its operational reliability, and machine learning algorithms play a crucial role in fault diagnosis. To achieve better fault diagnosis results, it is necessary to have a sufficient number of fault samples participating in the training of the model. In actual industrial scenarios, it is often difficult to obtain fault samples, and there may even be situations where no fault samples exist. For scenarios without fault samples, accurately identifying the unknown faults of equipment is an issue that requires focused attention. This paper presents a method for the normal-sample-based mechanical equipment unknown fault detection. By leveraging the characteristics of the autoencoder network (AE) in deep learning for feature extraction and sample reconstruction, normal samples are used to train the AE network. Whether the input sample is abnormal is determined via the reconstruction error and a threshold value, achieving the goal of anomaly detection without relying on fault samples. In terms of input data, the frequency domain features of normal samples are used to train the AE network, which improves the training stability of the AE network model, reduces the network parameters, and saves the occupied memory space at the same time. Moreover, this paper further improves the network based on the traditional AE network by incorporating a convolutional neural network (CNN) and a long short-term memory network (LSTM). This enhances the ability of the AE network to extract the spatial and temporal features of the input data, further improving the network's ability to extract and recognize abnormal features. In the simulation part, through public datasets collected in factories, the advantages and practicality of this method compared with other algorithms in the detection of unknown faults are fully verified.

Imbalanced sample fault diagnosis of rotating machinery using conditional variational auto-encoder generative adversarial network

Detection and classification are two significant tasks for industrial fault diagnosis. However, conventional methods typically treat these tasks as separate and independent problems, and necessitate a retraining process when new fault samples or classes are collected. Therefore, an incremental support vector data description scheme using Gaussian kernel function is pro-posed for industrial process fault diagnosis in a unified frame-work. In this framework, the decision boundary is updated incrementally only based on the specific original support vectors and newly collected samples. An adaptive threshold and a restructured radius are proposed to promote accuracy in the fault detection. In the classification procedure, the hyperspheres for all known classes are constructed by decision tree. The new sample that does not belong to any known class is identified as an unknown class. Without a time-consuming retraining process, the proposed diagnosis method with the incremental learning capability can synchronously achieve the fault detection and classification task. Experimental results demonstrate the effectiveness and superiority in terms of diagnosis performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In practical industrial processes, new fault samples are collected and new fault classes emerge continually. Under such scenarios, fault diagnosis with incremental learning capability is becoming increasingly important. This work proposes a unified incremental framework for fault diagnosis based on support vector data description, which is able to achieve fault detection and classification synchronously. For fault detection, an adaptive threshold and a restructured radius are developed. For fault classification, hypersphere-shaped boundaries of all fault classes are given via the decision tree-based strategy. The proposed method is updated to include new fault samples and fault classes without dimensionality reduction or any distributional assumptions.