Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function

One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.

Section: Methodsmentioning

confidence: 99%

Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques

Cruz

Rivas

Quiza

et al. 2020

“…In recent years, artificial intelligence has been extensively used to solve problems in different fields of human or natural activities. Artificial intelligence methods are widely used in a variety of engineering applications, for example, in artificial intelligence-based hole quality prediction in micro-drilling [1], or in the prognosis of bearing and gear wear using a convolutional neural network [2]. In addition, it can also be used to predict surface wear based on surface isotropy levels [3], or in the prediction of ore crushing-plate lifetimes [4].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, it can also be used to predict surface wear based on surface isotropy levels [3], or in the prediction of ore crushing-plate lifetimes [4]. New technologies like machine learning (ML) [5] and deep learning (DL) [2] take analytical work to the next level. This paper presents the implementation of the machine learning approach to predict earthquakes.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Modelling and Feature Engineering in Seismology Experiment

Брыков

Petryshynets

Pruncu

et al. 2020

This article aims to discusses machine learning modelling using a dataset provided by the LANL (Los Alamos National Laboratory) earthquake prediction competition hosted by Kaggle. The data were obtained from a laboratory stick-slip friction experiment that mimics real earthquakes. Digitized acoustic signals were recorded against time to failure of a granular layer compressed between steel plates. In this work, machine learning was employed to develop models that could predict earthquakes. The aim is to highlight the importance and potential applicability of machine learning in seismology The XGBoost algorithm was used for modelling combined with 6-fold cross-validation and the mean absolute error (MAE) metric for model quality estimation. The backward feature elimination technique was used followed by the forward feature construction approach to find the best combination of features. The advantage of this feature engineering method is that it enables the best subset to be found from a relatively large set of features in a relatively short time. It was confirmed that the proper combination of statistical characteristics describing acoustic data can be used for effective prediction of time to failure. Additionally, statistical features based on the autocorrelation of acoustic data can also be used for further improvement of model quality. A total of 48 statistical features were considered. The best subset was determined as having 10 features. Its corresponding MAE was 1.913 s, which was stable to the third decimal point. The presented results can be used to develop artificial intelligence algorithms devoted to earthquake prediction.

“…Shao et al [ 6 ] proposed a new deep-learning model that combines the advantages of the deep belief network (DBN) and convolutional neural network (CNN) to detect the bearing failure. Lo et al [ 24 ] propose a novel prognostic method based on a 1D CNN with clustering loss by classification training to detect bearing and gear wears. Chen et al [ 25 ] proposed a novel fault diagnosis method integrating CNN and ELM to reduce the training complexity and obtain robust features.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

Luo

Lin

Peng³

et al. 2020

Axle-box bearings are one of the most critical mechanical components of the high-speed train. Vibration signals collected from axle-box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF-CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one-dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF-CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.