Advancement in Data Engineering and Feature Processing Workflow by Using Deep Learning Techniques for the Automation of ESP Failure Root Cause Analyses

Today, Electrical Submersible Pump (ESP) failure analysis is a tedious, human-intensive, and time-consuming activity involving dismantle, inspection, and failure analysis (DIFA) for each failure. This paper presents a novel artificial intelligence workflow using an ensemble of machine learning (ML) algorithms coupled with natural language processing (NLP) and deep learning (DL). The algorithms outlined in this paper bring together structured and unstructured data across equipment, production, operations, and failure reports to automate root cause identification and analysis post breakdown. This process will result in reduced turnaround time (TAT) and human effort thus drastically improving process efficiency.

Sports and Health Management Using Big Data Based on Voice Feature Processing and Internet of Things

With the support of big data and information technology, various sectors such as sports, health, and medical industry can realize the integration and readjustment of the existing resources, which improve the operation efficiency of the industry and tap its huge potential. With the advancement in big data analysis, voice features, and Internet of Things (IoT), personalized health management is becoming the development trend and breakthrough of sports and health industry. The application of big data will tap out the huge potential of the sports and health industry. In this paper, we have used the Mel-requency cepstrum coefficient as the speech feature processing method. When the linear frequency is transformed to the Mel frequency by Fourier transform, the calculation accuracy will decrease with the increase in the frequency, and the low-frequency signal will be retained to improve the anti-noise ability. With further study of the voice feature processing and IoT model of big data’s sports and health management, a vector addition regression was developed to compare the two real scoring features of the processing results that pave the way for further analysis and result evaluation. Through experimental verification, it is proved that the method in this paper can better learn the speech features. At the same time, with the introduction of noise reduction, the big data of speech recognition in sports health management has a stronger robustness and improves the overall system performance.

3D Film Animation Image Acquisition and Feature Processing Based on the Latest Virtual Reconstruction Technology

In this paper, the latest virtual reconstruction technology is used to conduct in-depth research on 3D movie animation image acquisition and feature processing. This paper firstly proposes a time-division multiplexing method based on subpixel multiplexing technology to improve the resolution of integrated imaging reconstruction images. By studying the degradation effect of the reconstruction process of the 3D integrated imaging system, it is proposed to improve the display resolution by increasing the pixel point information of fixed display array units. According to the subpixel multiplexing, an algorithm to realize the reuse of pixel point information of 3D scene element image gets the element image array with new information; then, through the high frame rate light emitting diode (LED) large screen fast output of the element image array, the human eye temporary retention effect is used, so that this group of element image array information go through a plane display, to increase the limited display array information capacity thus improving the reconstructed image. In this way, the information capacity of the finite display array is increased and the display resolution of the reconstructed image is improved. In this paper, we first use the classification algorithm to determine the gender and expression attributes of the face in the input image and filter the corresponding 3D face data subset in the database according to the gender and expression attributes, then use the sparse representation theory to filter the prototype face like the target face in the data subset, then use the filtered prototype face samples to construct the sparse deformation model, and finally use the target faces. Finally, the target 3D face is reconstructed using the feature points of the target face for model matching. The experimental results show that the algorithm reconstructs faces with high realism and accuracy, and the algorithm can reconstruct expression faces.

Real-Time Arrhythmia Classification Algorithm Using Time-Domain ECG Feature Based on FFNN and CNN

To solve the problem of real-time arrhythmia classification, this paper proposes a real-time arrhythmia classification algorithm using deep learning with low latency, high practicality, and high reliability, which can be easily applied to a real-time arrhythmia classification system. In the algorithm, a classifier detects the QRS complex position in real time for heartbeat segmentation. Then, the ECG_RRR feature is constructed according to the heartbeat segmentation result. Finally, another classifier classifies the arrhythmia in real time using the ECG_RRR feature. This article uses the MIT-BIH arrhythmia database and divides the 44 qualified records into two groups (DS1 and DS2) for training and evaluation, respectively. The result shows that the recall rate, precision rate, and overall accuracy of the algorithm’s interpatient QRS complex position prediction are 98.0%, 99.5%, and 97.6%, respectively. The overall accuracy for 5-class and 13-class interpatient arrhythmia classification is 91.5% and 75.6%, respectively. Furthermore, the real-time arrhythmia classification algorithm proposed in this paper has the advantages of practicability and low latency. It is easy to deploy the algorithm since the input is the original ECG signal with no feature processing required. And, the latency of the arrhythmia classification is only the duration of one heartbeat cycle.

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and is also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for the effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that they may effectively contribute toward the implementation of DL models as applied to motor condition monitoring.