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Intelligent Designing, Measuring and Control for Frontier Instrument and Equipment

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 16334

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Special Issue Editors

Prof. Dr. Jing Liu

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Guest Editor

School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
Interests: gearbox; bearing; dynamics; vibrations; finite element bearing; power loss
Special Issues, Collections and Topics in MDPI journals

Dr. Jinglong Chen

E-Mail Website
Guest Editor

State Key Laboratory for Manufacturing and Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: fault diagnosis; condition monitoring; signal processing; deep learning; intelligent fault diagnosis; vibration analysis; residual life prediction; feature extraction; wavelet transform; adaptive signal processing

Dr. Yimin Chen

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Guest Editor

School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
Interests: dynamic modeling; intelligent control; model predictive control; robust control

Dr. Anil Kumar

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Assistant Guest Editor

College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China
Interests: condition monitoring; fault diagnosis; fault prognosis; vibration analysis; signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your original research or overview papers to this Special Issue on the “Intelligent Designing, Measuring and Control for Frontier Instrument and Equipment” published in Energies.

Advanced designing and control approaches, together with abundant measuring information, greatly enhance the performances of frontier instrument and equipment, regarding sustainability, vibration isolation, noise reduction, condition monitoring, fault diagnosis, autonomous behaviors, etc. Benefitting from interdisciplinary research into psychology, dynamics, system modeling, signal processing, control theory and computer science, intelligent designing, measuring and control approaches shed light in order to improve the intelligent level of frontier equipment, which paves the way to the future of smart equipment. However, the application and integrations of intelligent approaches, including neural networks, cognitive processes, fast computing, machine learning, and smart configuration, in frontier instrument and equipment are still facing great challenges and opening questions.

Due to additional sensing and communicating information, measuring and control approaches become complicated and complex in relation to vibration mechanism analysis, which impedes the knowledge of intelligent approaches from transferring to frontier equipment.

The main purpose of this special focus is to review state-of-the-art solutions to present challenges and discuss possible applications of intelligent approaches in designing, measuring, and control of frontier equipment. Topics of interest for this Special Issue include but are not limited to:

Vibration analysis, isolation, and reduction in frontier instrument and equipment;
Mechanical design, smart configuration, structural optimization of modern instrument and equipment;
Intelligent perceptive and measurement approaches of operational information;
Intelligent control and learning-based control approaches for frontier instrument and equipment;
Augmentation and interaction of designing, measurement and control;
Artificial intelligence, data fusion, signal processing and intelligence tests for frontier instrument and equipment;
Other intelligent applications in frontier instrument and equipment.

Prof. Dr. Jing Liu
Dr. Jinglong Chen
Dr. Yimin Chen
Dr. Anil Kumar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Vibration analysis
Structural design
Signal processing
Condition monitoring
Fault diagnosis
Intelligent control

Published Papers (7 papers)

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Research

21 pages, 20391 KiB

Open AccessArticle

Data Screening Based on Correlation Energy Fluctuation Coefficient and Deep Learning for Fault Diagnosis of Rolling Bearings

by Bo Qin, Quanyi Luo, Zixian Li, Chongyuan Zhang, Huili Wang and Wenguang Liu

Energies 2022, 15(7), 2707; https://doi.org/10.3390/en15072707 - 06 Apr 2022

Cited by 3 | Viewed by 1638

Abstract

The accuracy of the intelligent diagnosis of rolling bearings depends on the quality of its vibration data and the accuracy of the state identification model constructed accordingly. Aiming at the problem of “poor quality” of data and “difficult to select” structural parameters of the identification model, a method is proposed to integrate data cleaning in order to select effective learning samples and optimize the selection of the structural parameters of the deep belief network (DBN) model. First, by calculating the relative energy fluctuation value of the finite number of intrinsic function components using the variational modal decomposition of the rolling bearing vibration data, the proportion of each component containing the fault component is characterized. Then, high-quality learning samples are obtained through screening and reconstruction to achieve the effective cleaning of vibration data. Second, the improved particle swarm algorithm (IPSO) is used to optimize the number of nodes in each hidden layer of the DBN model in order to obtain the optimal structural parameters of the intelligent diagnosis model. Finally, the high-quality learning samples obtained from data cleaning are used as input to construct an intelligent identification model for rolling bearing faults. The results showed that the proposed method not only screens out the intrinsic mode function components that contain the fault effective components in the rolling bearing vibration data, but also finds the optimal solution for the number of nodes in the DBN hidden layer, which improves bearing state identification accuracy by 3%. Full article

(This article belongs to the Special Issue Intelligent Designing, Measuring and Control for Frontier Instrument and Equipment)

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31 pages, 11536 KiB

Open AccessArticle

Smart Energy Management System: Design of a Smart Grid Test Bench for Educational Purposes

by Oussama Laayati, Hicham El Hadraoui, Nasr Guennoui, Mostafa Bouzi and Ahmed Chebak

Energies 2022, 15(7), 2702; https://doi.org/10.3390/en15072702 - 06 Apr 2022

Cited by 16 | Viewed by 4663

Abstract

The presented article aims to design an educational test bench setup for smart grids and renewable energies with multiple features and techniques used in a microgrid. The test bench is designed for students, laboratory engineers, and researchers, which enables electrical microgrid system studies and testing of new, advanced control algorithms to optimize the energy efficiency. The idea behind this work is to design hybrid energy sources, such as wind power, solar photovoltaic power, hydroelectric power, hydrogen energy, and different types of energy storage systems such as batteries, pumped storage, and flywheel, integrating different electrical loads. The user can visualize the state of the components of each emulated scenario through an open-source software that interacts and communicates using OPC Unified Architecture protocol. The researchers can test and validate new solutions to manage the energy behavior in the grid using machine learning and optimization algorithms integrated in the software in form of blocks that can be modified and improved, and then simulate the results. A model-based system of engineering is provided, which describes the different requirements and case studies of the designed test bench, respecting the open-source software and the frugal innovation features in which there is use of low-cost hardware and open-source software. The users obtain the opportunity to add new sources and new loads, change software platforms, and communicate with other simulators and equipment. The students can understand the different features of smart grids, such as defect classification, energy forecasting, energy optimization, and basics of production, transmission, and consumption. Full article

(This article belongs to the Special Issue Intelligent Designing, Measuring and Control for Frontier Instrument and Equipment)

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16 pages, 3699 KiB

Open AccessArticle

A Study on the Modeling Method of Cage Slip and Its Effects on the Vibration Response of Rolling-Element Bearing

by Ya Luo, Wenbing Tu, Chunyu Fan, Lu Zhang, Yudong Zhang and Wennian Yu

Energies 2022, 15(7), 2396; https://doi.org/10.3390/en15072396 - 24 Mar 2022

Cited by 6 | Viewed by 1693

Abstract

Rolling-element bearings play vital roles in the dynamic vibration performance of the whole machinery. Hence, accurate modeling and assessment of the rolling-element bearing are beneficial for the well understanding of the vibration response of rolling-element bearing. However, cage slip is usually ignored in the traditional rolling-element bearing modeling methods, which has a direct influence on the rotating speed and friction force of the rolling elements. To settle the modeling problem of rolling-element bearing with cage slip, in this study, a nonlinear dynamic model with multiple degrees of freedom of the roller bearing is established. The cage slip, the motion of each roller, nonlinear contact, damping, and friction are taken into consideration. With the proposed method, a nonlinear traction model is presented to describe the friction forces induced by cage slip. Furthermore, both the friction force acting on rolling elements and the effects of cage slip on the vibration response are investigated based on the established model. Some comparisons between the proposed modeling method with cage slip and the classical method without cage slip are made. The results show that the friction force applied to the balls increases with the cage slip, friction coefficient, rotational speed, and radial load. A slight cage slip could be beneficial for reducing the vibration energy of rolling-element bearing; however, it will result in more friction loss and impact components. Full article

(This article belongs to the Special Issue Intelligent Designing, Measuring and Control for Frontier Instrument and Equipment)

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11 pages, 8911 KiB

Open AccessArticle

Reinforcement Learning Path Planning Method with Error Estimation

by Feihu Zhang, Can Wang, Chensheng Cheng, Dianyu Yang and Guang Pan

Energies 2022, 15(1), 247; https://doi.org/10.3390/en15010247 - 30 Dec 2021

Cited by 8 | Viewed by 2156

Abstract

Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. To address this problem, an improved path planning algorithm is proposed based on reinforcement learning method, which also calculates the characteristics of the cumulated error during the planning procedure. The cumulative error path is calculated by the map with convex target processing, while modifying the algorithm reward and punishment parameters based on the error estimation strategy. To verify the proposed approach, simulation experiments exhibited that the algorithm effectively avoid the error drift in path planning. Full article

(This article belongs to the Special Issue Intelligent Designing, Measuring and Control for Frontier Instrument and Equipment)

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13 pages, 942 KiB

Open AccessArticle

Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals

by Shu Han, Xiaoming Liu, Yan Yang, Hailin Cao, Yuanhong Zhong and Chuanlian Luo

Energies 2021, 14(22), 7702; https://doi.org/10.3390/en14227702 - 17 Nov 2021

Cited by 5 | Viewed by 1058

Abstract

With the development of modern industry and scientific technology, production equipment plays an increasingly important role in military and industrial production, and the fault detection signal of gears and bearings state in transmission equipment becomes very important. Therefore, this paper proposes a gear-bearing composite fault signal decomposition and reconstruction method, which combines the marine predator algorithm (MPA) and variational mode decomposition (VMD) technologies. For the parameters’ selection of VMD, the optimization algorithm allows us to quickly and accurately obtain the results with the best kurtosis correlation index after signal decomposition and reconstruction. The experiments demonstrate the excellent performance of our method in the field of separation and denoising mixed gear-bearing fault signals. Full article

(This article belongs to the Special Issue Intelligent Designing, Measuring and Control for Frontier Instrument and Equipment)

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18 pages, 6280 KiB

Open AccessArticle

Dynamic Influence of Wheel Flat on Fatigue Life of the Traction Motor Bearing in Vibration Environment of a Locomotive

by Bingbin Guo, Zhixiang Luo, Bo Zhang, Yuqing Liu and Zaigang Chen

Energies 2021, 14(18), 5810; https://doi.org/10.3390/en14185810 - 14 Sep 2021

Cited by 4 | Viewed by 1681

Abstract

Wheel flat can cause a large impact between the wheel and rail and excites a forced vibration in the locomotive and track structure systems. The working conditions and fatigue life of the motor bearings are significantly affected by the intensified wheel–rail interaction via the transmission path of the gear mesh. In this study, a fatigue life prediction method of the traction motor bearings in a locomotive is proposed. Based on the L−P theory or ISO 281 combined with the Miner linear damage theory and vehicle–track coupled dynamics, the irregular loads induced by the track random irregularity and gear mesh are considered in this proposed method. It can greatly increase the accuracy of predictions compared with the traditional prediction models of a rolling bearing life whose bearing loads are assumed to be constant. The results indicate that the periodic impact forces and larger mesh forces caused by the wheel flat will reduce the fatigue life of the motor bearings, especially when the flat length is larger than 30 mm. Using this method, the effects of the flat length and relatively constant velocity of the locomotive are analyzed. The proposed method can provide a theoretical basis to guarantee safe and reliable working for motor bearings. Full article

(This article belongs to the Special Issue Intelligent Designing, Measuring and Control for Frontier Instrument and Equipment)

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18 pages, 5293 KiB

Open AccessArticle

A Novel Method for Structural Lightweight Design with Topology Optimization

by Hongjun Xue, Haiyang Yu, Xiaoyan Zhang and Qi Quan

Energies 2021, 14(14), 4367; https://doi.org/10.3390/en14144367 - 20 Jul 2021

Cited by 8 | Viewed by 2402

Abstract

Topological optimization is an innovative method to realize the lightweight design. This paper proposes a hybrid topology optimization method that combines the SIMP (solid isotropic material with penalization) method and genetic algorithm (GA), called the SIMP-GA method. In the method, SIMP is used to update the chromosomes, which can accelerate convergence. The filtering scheme in the SIMP method can filter unconnected elements to ensure the connectivity of the structure. We studied the influence of varying the filtering radius on the optimized structure. Simultaneously, in the SIMP-GA method, each element is regarded as a gene, which controls the population number to a certain extent, reduces the amount of calculation, and improves the calculation efficiency. The calculation of some typical examples proves that the SIMP-GA method can obtain a better solution than the gradient-based method. Compared with the conventional genetic algorithm and GA-BESO (Bi-directional Evolutionary Structural Optimization) method, the calculation efficiency of the proposed method is higher and similar results are obtained. The innovative topology optimization method could be an effective way for structural lightweight design. Full article

(This article belongs to the Special Issue Intelligent Designing, Measuring and Control for Frontier Instrument and Equipment)

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