Fault Diagnosis Method for Engine Control System Based on Probabilistic Neural Network and Support Vector Machine

Wang, Bo; Ke, Hongwei; Ma, Xiaodong; Yu, Bing

doi:10.3390/app9194122

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…Support vector machines (SVMs) have been shown to achieve good generalization performance over a wide variety of classification problems, where it is seen that SVM tends to minimize generalization errors, that is, classifier errors over new instances. In geometric terms, SVM can be seen as the attempt to find a surface (σ i ) that separates positive examples from negative ones by the widest possible margin [22][23][24].…”

Section: Support Vector Machinesmentioning

confidence: 99%

Currency Crises Prediction Using Deep Neural Decision Trees

et al. 2019

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Featured Application: The superiority of a novel computational technique (deep neural decision trees) for prediction of currency crises over other methodologies and the construction of new crisis prediction models more precise than existing ones.Abstract: Currency crises are major events in the international monetary system. They affect the monetary policy of countries and are associated with risks of vulnerability for open economies. Much research has been carried out on the behavior of these events, and models have been developed to predict falls in the value of currencies. However, the limitations of existing models mean further research is required in this area, since the models are still of limited accuracy and have only been developed for emerging countries. This article presents an innovative global model for predicting currency crises. The analysis is geographically differentiated for regions, considering both emerging and developed countries and can accurately estimate future scenarios for currency crises at the global level. It uses a sample of 162 countries making it possible to account for the regional heterogeneity of the warning indicators. The method used was deep neural decision trees (DNDTs), a technique based on decision trees implemented by deep learning neural networks, which was compared with other methodologies widely applied in prediction. Our model has significant potential for the adaptation of macroeconomic policy to the risks derived from falls in the value of currencies, providing tools that help ensure financial stability at the global level. Appl. Sci. 2019, 9, 5227 2 of 18 effect of contagion on other countries. However, the event was about more than just speculation on the Thai currency and saw the collapse of Asian stock markets. The financial crisis that began with the devaluation of the Thai baht exchange rate resulted in a sharp increase in interest rates and the collapse of many companies, as well as an increase in the cost of credit and a general fall in GDP in the region [1]. This resulted in foreign and national investors pulling out investment. Not only did this crisis affect Asian countries, but it also had a negative impact on other emerging economies, especially in Latin America, showing that currency crises are not limited to a specific economy. Globalization can increase the economic difficulties of societies and affect the structure of national economies after the real economy has suffered a damaging impact [2]. Figure 1 shows the number of currency crises per year at the international level. Appl. Sci. 2019, 8, x FOR PEER REVIEW 2 of 19of contagion on other countries. However, the event was about more than just speculation on the Thai currency and saw the collapse of Asian stock markets. The financial crisis that began with the devaluation of the Thai baht exchange rate resulted in a sharp increase in interest rates and the collapse of many companies, as well as an increase in the cost of credit and a general fall in GDP in the region [1]. This resulted in foreig...

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Section: Support Vector Machinesmentioning

confidence: 99%

Currency Crises Prediction Using Deep Neural Decision Trees

et al. 2019

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“…The SVM is a machine learning algorithm based on the structural risk minimization principle [29]. SVM has achieved great performance in many pattern recognition based advanced technologies, such as the human-machine interface based on muscle activity classification [30], the brain-computer interface based on mental state classification [31] and the engine control system based on fault diagnosis [32]. The principle of SVM is introduced in brief.…”

Section: Classification With the Support Vector Machinementioning

confidence: 99%

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Jiang

Zhang

et al. 2019

Applied Sciences

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With the development of pervasive sensing and machine learning technologies, automated epileptic seizure detection based on electroencephalogram (EEG) signals has provided tremendous support for the lives of epileptic patients. Discrete wavelet transform (DWT) is an effective method for time-frequency analysis of EEG and has been used for seizure detection in daily healthcare monitoring systems. However, the shift variance, the lack of directionality and the substantial aliasing, limit the effects of DWT in some applications. Dual-tree discrete wavelet transform (DTDWT) can overcome those drawbacks but may increase information redundancy. For classification tasks with small dataset sizes, such redundancy can greatly reduce learning efficiency and model performance. In this work, we proposed a novel redundancy removed DTDWT (RR-DTDWT) framework for automated seizure detection. Energy and modified multi-scale entropy (MMSE) features in a dual tree wavelet domain were extracted to construct a complete picture of mental states. To the best of our knowledge, this is the first study to employ MMSE as an indicator of epileptic seizures. Moreover, a compact EEG representation can be obtained after removing useless information redundancy (redundancy between wavelet trees, adjacent channels and entropy scales) by a general auto-weighted feature selection framework via global redundancy minimization (AGRM). Through validation on Bonn and CHB-MIT databases, the proposed RR-DTDWT method can achieve better performance than previous studies.

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“…This method has been well applied in the fault detection of diesel engines. Wang et al [ 28 ] proposed the plan of particle swarm optimization probabilistic neural network (probabilistic neural network, PNN) and support vector machine. Effective diagnosis of common engine failures is achieved.…”

Section: Introductionmentioning

confidence: 99%

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning

Bai

Wen

et al. 2022

Sensors

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It is critical to deploy wireless data transmission technologies remotely, in real-time, to monitor the health state of diesel engines dynamically. The usual approach to data compression is to collect data first, then compress it; however, we cannot ensure the correctness and efficiency of the data. Based on sparse Bayesian optimization block learning, this research provides a method for compression reconstruction and fault diagnostics of diesel engine vibration data. This method’s essential contribution is combining compressive sensing technology with fault diagnosis. To achieve a better diagnosis effect, we can effectively improve the wireless transmission efficiency of the vibration signal. First, the dictionary is dynamically updated by learning the dictionary using singular value decomposition to produce the ideal sparse form. Second, a block sparse Bayesian learning boundary optimization approach is utilized to recover structured non-sparse signals rapidly. A detailed assessment index of the data compression effect is created. Finally, the experimental findings reveal that the approach provided in this study outperforms standard compression methods in terms of compression efficiency and accuracy and its ability to produce the desired fault diagnostic effect, proving the usefulness of the proposed method.

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Fault Diagnosis Method for Engine Control System Based on Probabilistic Neural Network and Support Vector Machine

Cited by 12 publications

References 15 publications

Currency Crises Prediction Using Deep Neural Decision Trees

Currency Crises Prediction Using Deep Neural Decision Trees

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning

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