Approximation Capability Analysis of Parallel Process Neural Network with Application to Aircraft Engine Health Condition Monitoring

In this article, a parametric model for health condition monitoring of wind turbines is developed. The study is based on the assumption that a wind turbine’s health condition can be modeled through three features: rotor speed, gearbox temperature and generator winding temperature. At first, three neural network models are created to simulate normal behavior of each feature. Deviation signals are then defined and calculated as accumulated time-series of differences between neural network predictions and actual measurements. These cumulative signals carry health condition–related information. Next, through nonlinear regression technique, the signals are used to produce individual models for considered features, which mathematically have the form of proportional hazard models. Finally, they are combined to construct an overall parametric health condition model which partially represents health condition of the wind turbine. In addition, a dynamic threshold for the model is developed to facilitate and add more insight in performance monitoring aspect. The health condition monitoring of wind turbine model has capability of evaluating real-time and overall health condition of a wind turbine which can also be used with regard to maintenance in electricity generation in electric power systems. The model also has flexibility to overcome current challenges such as scalability and adaptability. The model is verified in illustrating changes in real-time and overall health condition with respect to considered anomalies by testing through actual and artificial data.

Download Full-text

A Health Condition Monitoring Method of Aeroengine Gas Path System Based on Consistency Fusion and Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.385-386.981 ◽

2013 ◽

Vol 385-386 ◽

pp. 981-984

Author(s):

Jian Guo Cui ◽

Can Wu ◽

Li Ying Jiang ◽

Yi Wen Qi ◽

Guo Qiang Li

Keyword(s):

Neural Network ◽

Condition Monitoring ◽

Probabilistic Neural Network ◽

Complex Structure ◽

Reference Value ◽

Health Condition ◽

Monitoring Method ◽

Accurate Localization ◽

Safety And Reliability ◽

Path System

Because of the complex structure, poor working conditions and lots of fault modes of aeroengine , it is necessary to monitor the operational status, accurate localization of aeroengine fault and identify fault to improve the safety and reliability of aircraft. Based on consistency fusion, this paper uses probabilistic neural network to monitor health condition of aeroengine and puts forward a combined method of health condition monitoring based on the consistency fusion and the neural network. The results of test show that this method can quickly monitor the health condition of the aeroengine and has certain reference value for other mechanical equipments condition monitoring.

Download Full-text

Study on the Health Condition Monitoring Method of Hydraulic Pump Based on Convolutional Neural Network

2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) ◽

10.1109/icmtma50254.2020.00041 ◽

2020 ◽

Author(s):

Sun Shaowu ◽

Zhang Sheng ◽

Jiang Wanlu ◽

Li Zhenbao

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Condition Monitoring ◽

Health Condition ◽

Hydraulic Pump ◽

Monitoring Method

Download Full-text

Diamond Grinding Wheel Condition Monitoring Based on Acoustic Emission Signals

Sensors ◽

10.3390/s21041054 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1054

Author(s):

Guo Bi ◽

Shan Liu ◽

Shibo Su ◽

Zhongxue Wang

Keyword(s):

Neural Network ◽

Acoustic Emission ◽

Condition Monitoring ◽

Time Domain ◽

Diamond Wheel ◽

Feature Representation ◽

Grinding Wheel ◽

Time Waveform ◽

Original Time ◽

Whole Life Cycle

Acoustic emission (AE) phenomenon has a direct relationship with the interaction of tool and material which makes AE the most sensitive one among various process variables. However, its prominent sensitivity also means the characteristics of random and board band. Feature representation is a difficult problem for AE-based monitoring and determines the accuracy of monitoring system. It is knottier for the situation of using diamond wheel grinding optical components, not only because of the complexity of grinding process but also the high requirement on surface and subsurface quality. This paper is dedicated to AE-based condition monitoring of diamond wheel during grinding brittle materials and feature representation is paid more attention. AE signal of brittle-regime grinding is modeled as a superposition of a series of burst-type AE events. Theory analysis manifested that original time waveform and frequency spectrum are all suitable for feature representation. Considering the convolution form of b-AE in time domain, a convolutional neural network with original time waveform of AE signals as the input is built for multi-class classification of wheel state. Detailed state division in a wheel’s whole life cycle is realized and the accuracy is over 90%. Different from the overlapping in time domain, AE components of different crack mechanisms are probably separated in frequency domain. From this point of view, AE spectrums are more suitable for feature extraction than the original time waveform. In addition, the time sequence of AE samples is essential for the evaluation of wheel’s life elapse and making use of sequential information is just the idea behind recurrent neural network (RNN). Therefore, long short-term memory (LSTM), a special kind of RNN, is used to build a regression prediction model of wheel state with AE spectrums as the model input and satisfactory prediction accuracy is acquired on the test set.

Download Full-text

Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

Sensors ◽

10.3390/s21010262 ◽

2021 ◽

Vol 21 (1) ◽

pp. 262

Author(s):

Chih-Yung Huang ◽

Zaky Dzulfikri

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Template Matching ◽

Health Condition ◽

Vibration Measurement ◽

One Dimensional ◽

Stamping Process ◽

Metalworking Industry ◽

Increasing Demand ◽

Wear Condition

Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stamping cycle was selected through template matching. A one-dimensional (1D) convolutional neural network (CNN) was developed to classify the tool wear condition. The results revealed that the 1D CNN architecture a yielded a high accuracy (>99%) and fast adaptability among different models.

Download Full-text

Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211016505 ◽

2021 ◽

pp. 095440622110165

Author(s):

Zhang Chao ◽

Wang Wei-zhi ◽

Zhang Chen ◽

Fan Bin ◽

Wang Jian-guo ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Condition Monitoring ◽

Short Term Memory ◽

Vibration Signal ◽

Short Term ◽

Global Features ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

Download Full-text