scholarly journals A Study of Convolutional Neural Networks Learning Mechanisms for Machine Health Monitoring Applications

2020 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Mohammed Alabsi ◽  
Ahmad Saeed Mohammad ◽  
Ahmed Sammoud
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Health Monitoring ◽  
Band Pass Filter ◽  
Learning Mechanisms ◽  
Pass Filter ◽  
Wide Range ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Analytical Approaches

In recent years, Deep Learning (DL) and Internet of Things (IoT) technologies have been used and deployed jointly to solve a wide range of modern technical challenges in different areas. With the continuous advancement of IoT connectivity solutions, the range of applications that can benefit from such an increase is limitless. One area that can benefit significantly from the combined strength of DL and IoT technologies is Machine Health Monitoring (MHM) Systems. MHM utilizes different analytical approaches and tools to determine the state and health of different components in running machinery. The traditional MHM system uses control limits from predetermining values that determine if a component has failed depending on the preset limits of the machinery. The main disadvantage of using such technique us the unpredictable nature of the timing and component failure. This type of failure causes unplanned production time loss and increases the cost of maintenance due to the unpredictability of the failure events. With DL and low-cost sensors that use different IoT connectivity solutions, MHM systems can utilize the learning capabilities of the DL network to perform end-to-end prognosis. One crucial fact is that features learned by Deep Neural Networks (DNN) are part of a large black box, and there are valuable underlying physical meanings embedded within the features. Hence, there is an exciting research area to explore underlying mechanisms and interpret physical meanings within DNN. In this paper, DNN learning mechanisms are evaluated using three different models: stacked autoencoders (SAE), denoising autoencoders (DAE), and convolutional neural networks (CNN). Initial results indicate that the input layer behaves similarly to a band-pass filter.  However, deep layers require optimal input design to maximize neuron activation, which leads to an extensive understanding of deep layer learning consequently (In progress).

Neural-Network-Based System for Novel Fault Detection in Rotating Machinery

2004 ◽  
Vol 10 (8) ◽  
pp. 1137-1150 ◽  
Author(s):  
V. Crupi ◽  
E. Guglielmino ◽  
G. Milazzo
Keyword(s):  
Neural Networks ◽  
Health Monitoring ◽  
Rotating Machinery ◽  
Training Data ◽  
Training Set ◽  
Data Set ◽  
The Past ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Vibration Signatures

The purpose of this research is the realization of a method for machine health monitoring. The rotating machinery of the Refinery of Milazzo (Italy) was analyzed. A new procedure, incorporating neural networks, was designed and realized to evaluate the vibration signatures and recognize the fault presence. Neural networks have replaced the traditional expert systems, used in the past for the fault diagnosis, because they are a dynamic system and thus adaptable to continuously variable data. The disadvantage of common neural networks is that they need to be trained by real examples of different fault typologies. The innovative aspect of the new procedure is that it allows us to diagnose faults, which are not considered in the training set. This ability was demonstrated by our analysis; the net was able to detect the presence of imbalance and bearing wear, even if these typologies of faults were not present in the training data set.

scholarly journals Machine Health Monitoring and Fault Diagnosis Techniques Review in Industrial Power-Line Network

2020 ◽  
Author(s):  
Saud Altaf ◽  
Shafiq Ahmad
Keyword(s):  
Fault Diagnosis ◽  
Health Monitoring ◽  
Power Line ◽  
Noisy Signal ◽  
Industrial Environment ◽  
Power Bus ◽  
Wide Range ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Line Network

The machinery arrangements in industrial environment normally consist of motors of diverse sizes and specifications that are provided power and connected with common power-bus. The power-line could be act as a good source for travelling the signal through power-line network and this can be leave a faulty symptom while inspection of motors. This influence on other neighbouring motors with noisy signal that may present some type of fault condition in healthy motors. Further intricacy arises when this type of signal is propagated on power-line network by motors at different slip speeds, power rating and many faulty motors within the network. This sort of convolution and diversification of signals from multiple motors makes it challenging to measure and accurately relate to a certain motor or specific fault. This chapter presents a critical literature review analysis on machine-fault diagnosis and its related topics. The review covers a wide range of recent literature in this problem domain. A significant related research development and contribution of different areas regarding fault diagnosis and traceability within power-line networks will be discussed in detail throughout this chapter.

Adaptive Weighted Signal Preprocessing Technique for Machine Health Monitoring

2021 ◽  
Vol 70 ◽  
pp. 1-11
Author(s):  
Bingchang Hou ◽  
Dong Wang ◽  
Yi Wang ◽  
Tongtong Yan ◽  
Zhike Peng ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Signal Preprocessing ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Preprocessing Technique

Machine Health Monitoring of Induction Motors

2019 ◽  
pp. 51-56
Author(s):  
Panchaksharayya S. Hiremath ◽  
Kalyan Ram B. ◽  
Santoshgouda M. Patil ◽  
V. Sabarish ◽  
Preeti Biradar ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Induction Motors ◽  
Machine Health Monitoring ◽  
Machine Health

scholarly journals Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Aerospace ◽  
2020 ◽  
Vol 7 (12) ◽  
pp. 171
Author(s):  
Anil Doğru ◽  
Soufiane Bouarfa ◽  
Ridwan Arizar ◽  
Reyhan Aydoğan
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Visual Inspection ◽  
Hybrid Approach ◽  
Lightning Strike ◽  
Aircraft Maintenance ◽  
Original Dataset ◽  
Wide Range ◽  
Augmentation Techniques

Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Though supporting aircraft maintenance engineers detect and classify a wide range of defects, the time spent on inspection can significantly be reduced. Examples of defects that can be automatically detected include aircraft dents, paint defects, cracks and holes, and lightning strike damage. Additionally, this concept could also increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure. In our previous work, we have applied a recent Convolutional Neural Network architecture known by MASK R-CNN to detect aircraft dents. MASK-RCNN was chosen because it enables the detection of multiple objects in an image while simultaneously generating a segmentation mask for each instance. The previously obtained F1 and F2 scores were 62.67% and 59.35%, respectively. This paper extends the previous work by applying different techniques to improve and evaluate prediction performance experimentally. The approach uses include (1) Balancing the original dataset by adding images without dents; (2) Increasing data homogeneity by focusing on wing images only; (3) Exploring the potential of three augmentation techniques in improving model performance namely flipping, rotating, and blurring; and (4) using a pre-classifier in combination with MASK R-CNN. The results show that a hybrid approach combining MASK R-CNN and augmentation techniques leads to an improved performance with an F1 score of (67.50%) and F2 score of (66.37%).

Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

2018 ◽  
Vol 65 (2) ◽  
pp. 1539-1548 ◽  
Author(s):  
Rui Zhao ◽  
Dongzhe Wang ◽  
Ruqiang Yan ◽  
Kezhi Mao ◽  
Fei Shen ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Local Feature ◽  
Feature Based ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Gated Recurrent Unit
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