Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis

Gao, Zehai; Ma, Cunbao; Song, Dong; Liu, Yang

doi:10.1016/j.neucom.2017.01.032

Cited by 69 publications

(35 citation statements)

References 41 publications

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“…Step 4: Fault diagnosis of the system based on the multiple fault dependency matrix. For the multiple fault dependency matrix shown in Table 3, delete the row vector of the fault type with '0' including f 2 , f 3 , f 4 , f (2,3) , and f (2,3,4) . After that, the the fault ambiguity group G can be formulated, as shown in Table 4.…”

Section: Fault Type T 2 T 3 Tmentioning

confidence: 99%

“…Fault diagnosis is the basis of the maintenance work for complex equipment and systems, such as the components in an aircraft [1,2], a motor [3][4][5], a diesel engine [6], and so on [7]. In practical applications, multiple fault states may exist in a complex system, while various available tests can be applied for fault detection [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Dependency Model-Based Multiple Fault Diagnosis Using Knowledge of Test Result and Fault Prior Probability

Zhou

Ma³

et al. 2019

Applied Sciences

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Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior probability of each fault type is proposed. Firstly, the dependency model of the system can be built and used to formulate the fault-test dependency matrix. Then, the dependency matrix is simplified according to the knowledge in the test results of the system. After that, the logic ‘OR’ operation is performed on the feature vectors of the fault status in the simplified dependency matrix to formulate the multiple fault dependency matrix. Finally, fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status. An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method.

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Section: Fault Type T 2 T 3 Tmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dependency Model-Based Multiple Fault Diagnosis Using Knowledge of Test Result and Fault Prior Probability

Zhou

Ma³

et al. 2019

Applied Sciences

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“…Failure or fault in fuel system causes instability to the engine and in the auxiliary power unit. Gao et al [22] uses Deep Quantum Inspired Neural Network (DQINN), a method inspired by Deep Quantum Network (DQN) [43] for aircraft fuel system fault diagnosis. DQINN is a combination of DBN and Quantum Inspired Neural Network (QINN) [44].…”

Section: Deep Belief Networkmentioning

confidence: 99%

Deep Learning Approaches to Aircraft Maintenance, Repair and Overhaul: A Review

Rengasamy

Morvan

Figueredo

2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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The use of sensor technology constantly gathering aircrafts' status data has promoted the rapid development of data-driven solutions in aerospace engineering. These methods assist, for instance, with determining appropriate actions for aircraft maintenance, repair and overhaul (MRO). Challenges however are found when dealing with such large amounts of data. Identifying patterns, anomalies and faults disambiguation, with acceptable levels of accuracy and reliability are examples of complex problems in this area. Experiments using deep learning techniques, however, have demonstrated its usefulness in assisting on the analysis aircraft health data. The purpose of this paper therefore is to conduct a survey on deep learning architectures and their application in aircraft MRO. Although deep learning in general is not yet largely exploited for aircraft health, from our search, we identified four main architectures employed to MRO, namely, Deep Autoencoders, Long Short-Term Memory, Convolutional Neural Networks and Deep Belief Networks. For each architecture, we review their main concepts, the types of problems to which these architectures are employed to, the type of data used and their outcomes. We also discuss how research in this area can be advanced by identifying current research gaps and outlining future research opportunities.

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“…Since the feature set is obtained by learning and minimizing the loss function, it can be considered that this feature set is the best choice for this classification task [37]. The most representative deep learning algorithms include the deep belief network (DBN) [38,39] based on the restricted Boltzmann machine (RBM), the convolutional neural network (CNN) [40][41][42] based on the convolution layer and pooling layer and the recurrent neural network (RNN) [43,44] based on the recursive layer. They have made remarkable achievements in machine vision, natural language processing and fault diagnosis [45].…”

Section: Introductionmentioning

confidence: 99%

Statistical Process Control with Intelligence Based on the Deep Learning Model

Zan

Liu

et al. 2019

Applied Sciences

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Statistical process control (SPC) is an important tool of enterprise quality management. It can scientifically distinguish the abnormal fluctuations of product quality. Therefore, intelligent and efficient SPC is of great significance to the manufacturing industry, especially in the context of industry 4.0. The intelligence of SPC is embodied in the realization of histogram pattern recognition (HPR) and control chart pattern recognition (CCPR). In view of the lack of HPR research and the complexity and low efficiency of the manual feature of control chart pattern, an intelligent SPC method based on feature learning is proposed. This method uses multilayer bidirectional long short-term memory network (Bi-LSTM) to learn the best features from the raw data, and it is universal to HPR and CCPR. Firstly, the training and test data sets are generated by Monte Carlo simulation algorithm. There are seven histogram patterns (HPs) and nine control chart patterns (CCPs). Then, the network structure parameters and training parameters are optimized to obtain the best training effect. Finally, the proposed method is compared with traditional methods and other deep learning methods. The results show that the quality of extracted features by multilayer Bi-LSTM is the highest. It has obvious advantages over other methods in recognition accuracy, despite the HPR or CCPR. In addition, the abnormal patterns of data in actual production can be effectively identified.

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Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis

Cited by 69 publications

References 41 publications

Dependency Model-Based Multiple Fault Diagnosis Using Knowledge of Test Result and Fault Prior Probability

Dependency Model-Based Multiple Fault Diagnosis Using Knowledge of Test Result and Fault Prior Probability

Deep Learning Approaches to Aircraft Maintenance, Repair and Overhaul: A Review

Statistical Process Control with Intelligence Based on the Deep Learning Model

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