Complex Electromechanical System Research Articles

Organizational Level Cloud Diagnostics System of Complex Electromechanical System Based on SOA

In order to better solve the problem of insufficient support ability of high-tech electromechanical system, the cloud computing model was introduced into fault diagnostics system of electromechanical system. An open, loose-coupled architecture of Integrated Diagnostic System (IDS) for electromechanical system is put forward basing on Service- Oriented Cloud Computing Architecture (SOCCA). Test and diagnosis resources of repair shop at all levels and total life cycle information resources of electromechanical system were integrated by Portable Maintenance Aid (PMA) with cloud diagnostics platform. According to different demands, Test and diagnosis resources can be divided into data, software, platform and infrastructure, which can be provided on demand to maintainer of all levels as a service. This allows more complex maintenance issues to be accomplished at Forward Operating Locations (FOLs) by better utilizing the most relevant diagnostic information to enable direct, real-time, and engineering assistance when needed. Organizational Level (O-Level) IDS can effectively improve O-level maintainer support ability of complex electromechanical system.

An artificial immune and incremental learning inspired novel framework for performance pattern identification of complex electromechanical systems

Performance pattern identification is the key basis for fault detection and condition prediction, which plays a major role in ensuring safety and reliability in complex electromechanical systems (CESs). However, there are a few problems related to the automatic and adaptive updating of an identification model. Aiming to solve the problem of identification model updating, a novel framework for performance pattern identification of the CESs based on the artificial immune systems and incremental learning is proposed in this paper to classify real-time monitoring data into different performance patterns. First, an unsupervised clustering technique is used to construct an initial identification model. Second, the artificial immune and outlier detection algorithms are applied to identify abnormal data and determine the type of immune response. Third, incremental learning is employed to trace the dynamic changes of patterns, and operations such as pattern insertion, pattern removal, and pattern revision are designed to realize automatic and adaptive updates of an identification model. The effectiveness of the proposed framework is demonstrated through experiments with the benchmark and actual pattern identification applications. As an unsupervised and self-adapting approach, the proposed framework inherits the preponderances of the conventional methods but overcomes some of their drawbacks because the retraining process is not required in perceiving the pattern changes. Therefore, this method can be flexibly and efficiently used for performance pattern identification of the CESs. Moreover, the proposed method provides a foundation for fault detection and condition prediction, and can be used in other engineering applications.

Fault correlation analysis-based framework for reliability deployment of electromechanical system

Reliability allocation is a very critical step of product development process for setting achievable reliability goals. However, most research on reliability allocation have ignored the correlation between faults in a complex electromechanical system. In view of this problem, this work proposed an optimizationmethod of reliability allocation by combining Quality Function Deployment (QFD) and Failure Tree Analysis (FTA). First, a fault tree of the system is build, and failure mode of the system is discovered in the bottom events of the fault tree. Second, the failure mode in previous step is input into House of Reliability (HoR), and the correlation of faults and fault severity could be calculated in the HoR. Finally, according to the results of HoR, the fault severity of each component is transformed into the importance degree, and the system reliability target is allocated to each component on the basis of the importance degree. A case study of a transmission agent was conducted to illustrate the analysis and application processes of the proposed method. The result showed that this method made the distribution more reasonable by considered the correlation between failures.

Interest and Applicability of Meta-Heuristic Algorithms in the Electrical Parameter Identification of Multiphase Machines

Multiphase machines are complex multi-variable electro-mechanical systems that are receiving special attention from industry due to their better fault tolerance and power-per-phase splitting characteristics compared with conventional three-phase machines. Their utility and interest are restricted to the definition of high-performance controllers, which strongly depends on the knowledge of the electrical parameters used in the multiphase machine model. This work presents the proof-of-concept of a new method based on particle swarm optimization and standstill time-domain tests. This proposed method is tested to estimate the electrical parameters of a five-phase induction machine. A reduction of the estimation error higher than 2.5% is obtained compared with gradient-based approaches.

IDENTIFICATION OF A BACKLASH ZONE IN AN ELECTROMECHANICAL SYSTEM CONTAINING CHANGES OF A MASS INERTIA MOMENT BASED ON A WAVELET–NEURAL METHOD

In this article a new method of identification of a backlash zone width in a structure of an electromechanical system has been presented. The results of many simulations in a tested model of a complex electromechanical system have been taken while changing a value of a reduced masses inertia moment on a shaft of an induction motor drive. A wavelet analysis of tested signals and analysis of weights that have been obtained during a neural network supervised learning - have been applied in a diagnostic algorithm. The proposed algorithm of detection of backlash zone width, represents effective diagnostic method of a system at changing dynamic conditions, occurring also as a result of mass inertia moment changes.

Analysis of the electromechanical coupling mechanism and torsional vibration characteristics of a high‐speed train drive system

The high-speed train transmission system is a complex electromechanical coupling system, which consists of a motor, a universal shaft, a gearbox, and a wheel set. In order to solve dynamic problems such as torsion vibration caused by a high-speed train transmission system, the simplified model and equivalent mechanics model of the system were established, the natural vibration frequency was solved, and the simulation results of the system under a fixed torque input were analysed. The results showed that the natural vibration frequency of the universal shaft was 4.19 Hz, the natural vibration frequency of the pinion was 7.54 Hz, the natural vibration frequency of the big gear was 35.6 Hz, and the natural vibration frequency of the wheel set was 107.45 Hz. The natural vibration frequency of each part of the system increased gradually during the transmission from the motor to the wheel set. A fixed torque was added to the input end of the simulation model. The simulation results showed that the change amplitude of the torsion angle displacement of the big gear and the wheel set decreases. It is known that the angular displacement caused by the torsional vibration is weakened by the larger torsion stiffness of the axle.

Investigation of the Dynamic Behavior of the Electric Drive of a Winding Device of a Line for the Production of Bulk Nonwoven Fabric

The complex electromechanical control system of the winding device of a line used for the production of bulk nonwoven fabric is investigated. A functional diagram of the winding device that supports realization of a given speed of operation with constant tension of the fibrous product is developed. The basic drawbacks of the automatic compensation system of the winding device of the line are formulated and ways of eliminating these drawbacks with the use of smart technologies are determined.

IDENTIFICATION OF THE MASS INERTIA MOMENT IN AN ELECTROMECHANICAL SYSTEM BASED ON WAVELET–NEURAL METHOD

This paper presents the results of testing of a complex electromechanical system model. These results have been obtained for accepted in simulations the method of identifying an inertia moment of reduced masses on shaft of induction motor drive during the changes of a backlash zone width. The effectiveness of correct diagnostic conclusions enables coefficients analysis of testing signals wavelet expansion as well as weights of a supervised learning neural network. The earlier fault detection of five important state variables, which describe physical quantities of chosen complex electromechanical system has been verified for its correctness during the backlash zone width monitoring in the early stage of its gradual rise. The proposed here algorithm with mass inertia moment changes has proved to be an effective diagnostic method in the area of system changeable dynamic conditions and this has been shown in the resulting changes of backlash zone width.

System Reliability Assessment Based on Failure Propagation Processes

One or several component failures may lead to more related component malfunction and ultimately cause system reliability reduction. Based on this, we focus on the assessment system reliability of complex electromechanical systems (CEMSs) in a fault-propagation view. First, failure propagation model taking into consideration failure data based on network theory and improved polychromatic sets is proposed for system reliability evaluation. From the node point of view, system effectiveness index is constructed to investigate the variation of efficiency of the holistic network. Subsequently, from the system’s perspective, system reliability measurement is provided and estimated in combination with system effectiveness index and failure propagation models. Finally, the application of proposed method to a bogie system of high-speed train assesses system reliability, and meanwhile, the effectiveness of the proposed method is able to be illustrated.

Data fusion combined with echo state network for multivariate time series prediction in complex electromechanical system

Multivariate time series is widely used to evaluate and predict the health state of system. To focus on the importance of prediction accuracy for multivariate time series, many a research have been done on improving the performance of predictive model. However, for multivariate time series, the prediction accuracy depends not only on predictive model but also on the input information. In this paper, we combine data fusion with ESN to improve the prediction accuracy of multivariate time series, which focuses on both the processing of the input information of predictive model and the optimizing of predictive model. First, multi-sources data fusion is presented to obtain the new input for prediction model before predicting; phase space reconstruction and self-adaptive weighted fusion algorithm are adopted to fuse multivariate time series and obtain more complete information. Then, leaky rectifier liner units are used to replace the original activation function, tanh, and locality preserving projection is employed to optimize the state matrix of the reservoir. Finally, the effectiveness of the proposed method is verified by an analysis of one case study of real compressor groups data sets in chemical production system. The results and a comparison with the traditional method show that the proposed method can greatly enhance the prediction accuracy of multivariate time series and the one-step ahead prediction accuracy is improved by three orders of magnitude as well as a better generalization ability is obtained in the multi-steps ahead prediction.

Reliability assessment of complex electromechanical systems: A network perspective

AbstractThe network theory is widely applied to improve the reliability of a complex electromechanical system. In this application, system reliability assessment with network theory has been paid a great deal of attention. Because of instrument malfunctions, staff omissions, imperfect inspection strategies, and complex structures, field failure data are often subject to interval censoring, making the holistic reliability assessment becomes a difficult task. Most traditional methods assume reliability of critical components or partial reliability as system reliability, which may cause a large bias in system reliability estimation. This paper proposes a novel method to evaluate and predict the system reliability of a complex electromechanical system subject to the insufficient fault data problem from a network perspective. First, the system modeling based on network theory is developed to describe the topology of a holistic system. Second, interval‐valued intuitionistic hesitant fuzzy number is proposed in order to solve insufficient data for single component. Then, a new measure—comprehensive reliability—that can reflect the reliability of nodes in combination with functional properties and topological properties, which are formulated by failure data and network model, respectively, is constructed for system reliability assessment. Subsequently, an improved system reliability model based on percolation theory is given in terms of comprehensive reliability of nodes. Finally, to verify the effectiveness of the proposed method, a simulation and a real case study for traction system are implemented.

Fault diagnosis method of large-scale complex electromechanical system based on extension neural network

Owing to the complex fault information features in the operation of complex large-scale electromechanical systems, the existing semi-artificial state detection method is unable to obtain fast, accurate, or reliable diagnosis results, which causes significant loss to engineering construction projects. To solve this problem, a fault diagnosis method for a shield machine system based on an extension neural network is proposed. Using a matter-element model to describe the fault characteristic information, the upper and lower bounds of the classical domain of the characteristic index are used as the double weights of the neural network. Then, a fault diagnosis is made using the extension distance as the measuring tool, the algorithm structure and flow are constructed, the steps of the algorithm are given, and the effectiveness of the method is verified using tunnel boring machine as an example. The simulation results show that this method simplifies the network structure, improves the convergence speed, and obtains consistent diagnostic results. This research can provide a new method and idea for solving the problems of time, and manpower consumption, as well as the cost, of a fault diagnosis of a semi-automatic shield machine system.

Intuitionistic Mechanism for weak components identification method of complex electromechanical system

Intuitionistic Mechanism for weak components identification method of complex electromechanical system

Computer-aided design system for control moment gyroscope

Control moment gyroscope or two-axis balking gyroscope is used in spacecraft orientation systems. The control moment gyroscope is a complex electromechanical system with different parameters which connect by means of specific instant and inverse dependences. This fact could complicate the design process as of getting the optimal parameters the search of construction variations becomes harder. To simplify the control moment gyroscope designing process and increase its efficiency the computer-aided design system could be as highly useful. The computer-aided design system which is used in the present research and described in the article consists of five main subsystems. They cover the main stages of control moment gyroscope designing.

Super Twisting Control For Thermo’s Catalyst-5 Robotic Arm

The control of industrial robotics systems is important due to the wide range of their applications. These machines are complex electromechanical systems that have multiples input/outputs, are non-linear and uncertainty. For this purpose, it is essential that the robot programmers can test the behavior of the robots in different circumstances and with varying parameters under a control methodology. In this work, it is designed a controller base on Super Twisting Algorithm (robust nonlinear controller) to reduce the chattering problem of sliding mode control with the robustness of robotic manipulator, and the stability proof in the case of nonlinear systems. Simulation results are shown and compare with the Proportional-Derivative controller using the 5-DOF (Degrees of Freedom) robotic manipulator CRS Catalyst-5 by Thermo Electron Corporation to illustrate the efficient of the proposed method.

Comparison of Non-linear Signals Analysis Methods

In modern engineering, linear is relative, while non-linear and non-stationary is absolute. There are many methods in non-linear signals processing. How to select a most suitable analysis method quickly for the nonlinear signal is particularly important, which can improve the signal processing efficiently. In this paper, three common analysis methods for nonlinear signals, Wavelet spectrum, Hilbert spectral analysis (HSA) and Poincaré mapping are researched and analyzed by some typical nonlinear signals from the complex electromechanical model test system. The effectiveness and application scopes of these approaches are obtained, which can provide a theoretical and practical basis for engineering application.

Application research of multivariate linkage fluctuation analysis on condition evaluation in process industry

Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study, we present a straightforward and fast computational method, the multivariable linkage coarse graining (MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.

Adaptive Nonsingular Fast Terminal Sliding Mode Control for Braking Systems with Electro-Mechanical Actuators Based on Radial Basis Function

In this paper an adaptive non-singular fast terminal sliding mode (NFTSM) control scheme is proposed to control the electro-mechanical actuator (EMA) in an electric braking system which is a complex electro-mechanical system. In order to realize high-performance brake pressure servo control, a radial basis function (RBF) neural network method is adopted to deal with the difficulty of estimating the upper bound of the compound disturbance in the system, to reduce the conservatism of the design of sliding mode switching gain, and effectively eliminate sliding mode chattering. The simulation results show that, compared with a linear controller, the proposed control strategy is able to improve the servo performance and control precision. In addition the response speed of the braking actuator is enhanced significantly, without changing the traditional double-loop control structure.

An information transfer based novel framework for fault root cause tracing of complex electromechanical systems in the processing industry

Abstract As one of the most important approaches for analyzing the mechanism of fault pervasion, fault root cause tracing is a powerful and useful tool for detecting the fundamental causes of faults so as to prevent any further propagation and amplification. Focused on the problems arising from the lack of systematic and comprehensive integration, an information transfer-based novel data-driven framework for fault root cause tracing of complex electromechanical systems in the processing industry was proposed, taking into consideration the experience and qualitative analysis of conventional fault root cause tracing methods. Firstly, an improved symbolic transfer entropy method was presented to construct a directed-weighted information model for a specific complex electromechanical system based on the information flow. Secondly, considering the feedback mechanisms in the complex electromechanical systems, a method for determining the threshold values of weights was developed to explore the disciplines of fault propagation. Lastly, an iterative method was introduced to identify the fault development process. The fault root cause was traced by analyzing the changes in information transfer between the nodes along with the fault propagation pathway. An actual fault root cause tracing application of a complex electromechanical system is used to verify the effectiveness of the proposed framework. A unique fault root cause is obtained regardless of the choice of the initial variable. Thus, the proposed framework can be flexibly and effectively used in fault root cause tracing for complex electromechanical systems in the processing industry, and formulate the foundation of system vulnerability analysis and condition prediction, as well as other engineering applications.

Interaction analysis–based information modeling of complex electromechanical systems in the processing industry

Information modeling for complex electromechanical systems in the processing industry is the foundation for system vulnerability analysis, failure propagation mechanism, and fault root cause tracing driven by data analysis. Focusing on the difficulties in information modeling for complex electromechanical systems, a new approach based on interaction analysis and a general framework for information modeling of complex electromechanical systems are proposed. First, the basic structures of the information model are defined based on monitoring data. Second, an improved symbolic transfer entropy method with procedures for optimizing the number of symbols, phase space reconstruction, binary encoding, and decimal decoding are proposed to detect the interaction direction and quantify the interaction strength between different monitoring variables. Third, some optional methods are introduced to simplify and modify models. Finally, a directed-weighted information model for a specific complex electromechanical system is constructed based on information flow. An actual information modeling application of a complex electromechanical system is used to demonstrate the proposed method and compare it with existing methods. This new approach can handle general information modeling problems and overcome the drawbacks of existing methods since all the monitoring variables are used to improve integrity of the model, and prior knowledge about the physical structure and key points’ selection are not required. A unique and complete information model is obtained regardless of the choice of the initial variable. Thus, the proposed method can be flexibly and effectively used in information modeling for complex electromechanical systems in the processing industry and formulate the foundation of system vulnerability analysis, failure propagation mechanism, and fault root cause tracing methodology, as well as other engineering applications.