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Testability growth is a process that aims to improve the testability level of the equipment via identifying and removing the testability design defects (TDDs). The establishment of the existing testability growth model (TGM) needs to consider a variety of factors, it's difficult to describe it accurately. To solve this problem, a TGM based on evidential reasoning (ER) method with nonlinear optimization...
Due to the limited data variables of the spacecraft solar array, a comparison was made between the maximum power point voltage and current data of the solar array in the case of normal, short circuit, and irradiation failure. The maximum power point voltage and current data are used as the description of faults as the basis for fault diagnosis. Because of the cross-over phenomenon of the fault samples,...
Nowadays, fault detect and prediction is quite important for the purpose of ensuring the correct functioning of complex system; nevertheless, it is usually difficult to establish an exact mathematical model in analytical form for complex system, therefore, fault prediction of complex system always relays on the analysis of the observed chaotic time series. In order to enhance the validity and accuracy...
For the randomness and uncertainty of fault for Radar transmitter, a prognostic method based on discrete Hidden Markov Model (DHMM) is proposed. In the paper, three monitoring parameters of transmitter are collected and a discrete Hidden Markov Model is established. In order to have a fast convergence, The Baum-Welch (B-W) algorithm is used for training of DHMM. Finally, the state probability transition...
This paper proposes an improved method for DBN, by means of introducing the detachment rate. The introduction of detachment rate can play a similar average role, and can make the complex relationship between the neurons weakened, so that DBN learning has stronger robustness. Three kinds of data (corresponding to healthy, faulted and deteriorating) were classified by the improved depth belief network...
Currently, most machine learning methods applied in spacecraft fault diagnosis are supervised learning methods, which have obvious shortcomings in representing complex functions under limited samples and cells. Moreover, the generalization ability of these methods is restricted. This article proposes a deep machine learning based method on satellite power system fault diagnosis, which combines the...
To assess the multi-state of a rolling bearing more effectively and simultaneously, a unified assessment method is proposed based on chaos fruit fly optimization algorithm hyper-sphere support vector machine (CFOA-HSVM) two measures combination. Aiming to the blindness of parameters selection for HSVM, multiple parameters of HSVM can be searched the optimal values using chaos theory combined with...
Expected product quality is affected by multi-parameter in complex manufacturing processes. Product quality prediction can offer the possibility of designing better system parameters at the early production stage. Many existing approaches fail at providing favorable results duo to shallow architecture in prediction model that can not learn multi-parameter's features insufficiently. To address this...
As a valid method of time-frequency analysis, Wavelet transform (WT) can offer great help for gearbox fault diagnosis. However, it requires much human expertise and prior knowledge to diagnose the faulty conditions of gearbox according to the time-frequency distribution. In addition, the coupling of different failures and noise makes it hard to accurately diagnose the running conditions of the gearbox...
Fault diagnosis is significant to induction motor which has been widely used as industrial power driving sources. By fault diagnosis, proper maintenance can be arranged to avoid accidents, ensure safety and reduce maintenance costs. However, variable operating conditions and background noise always reduce effectiveness of traditional fault diagnosis methods. Currently the most advanced machine learning...
The safety and reliability of roller bearing always have significant importance in rotating machinery. It is needful to build an efficient and excellent accuracy method to monitoring and diagnosis the baring failure. A novel method is presented in this paper to classify the fault feature by wavelet function and extreme learning machine(ELM) that take into account the high accuracy and efficient. The...
The issue of remaining useful life (RUL) prediction has already become a quite interesting topic in industrial product. The data driven RUL prediction has been applied to the current research by taking advantage of a long-short term memory (LSTM)-recurrent neural network (RNN) approach. This means that even in a specified long-short term memory bound and limited available data sets, the RUL predictions...
Effective fault diagnosis method has long been a hot topic in the field of prognosis and health management of rotary machinery. This paper investigates an effective deep learning method known as sparse filtering, which is used to extract features from fault signal directly. And then, the supervised learning method softmax regression is applied to classify the fault types. The training samples are...
The performance conditions of steam turbine regenerative system have important influence on the safety and economy of the units. It is of great significance to doing the research on the performance monitoring of the regenerative system to ensure the safe and economical operation of the whole coal-fired power plants. In view of the shortcomings of the complexity of traditional performance monitoring...
As a breakthrough in artificial intelligence, deep learning allows for the automatic extraction of features without considerable prior knowledge and the determination of the complex non-linear relationship of the input parameters. Owing to these advantages, deep neural networks (DNNs) are superior to traditional artificial neural networks with shallow architectures, and are thus becoming widely used...
Aiming at some characteristics of servo valves that the complex influence between the failure modes and the high order nonlinearity of the fault datum, this paper presents a fault diagnosis model-Deep Intelligent Generalized Regression Neural Network (DGN). The DGN is a supervised deep learning model. In order to fully learning the fault datum, this paper proposed a logistic mapping and dynamic step...
To overcome the limitations of manual features and obtain the operating characteristics of the equipment in complex operation processes, different deep learning models have been utilized for industrial data, improving classification accuracy yet causing some other limitations meanwhile. In this paper, a deep hybrid model named Stochastic Convolutional and Deep Belief Network (SCDBN), which assembles...
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