Identification Of Machine Parameters Research Articles

Dynamic Model-Embedded Intelligent Machine Fault Diagnosis Without Fault Data

Intelligent machine fault diagnosis technique has recently exploded interest in digital health, energy power, and industrial maintenance. Collecting machine fault data in engineering practice is usually costly, causing big challenges in the intelligent diagnosis of most fresh-from-the-factory machines that are missing fault data. Inspired by the main idea of the digital twin, this paper proposes a dynamic model-embedded intelligent machine fault diagnosis framework. Specifically, a machine dynamic modeling and parameter identification approach is introduced for building the machine digital model. The digital model is used to predict machine fault data from the measured healthy vibration signals. Furthermore, a parameterized convolutional neural network structure is designed for learning optimization features and recognizing the machine's healthy state. Experimental investigation demonstrates the effectiveness of the framework. The results show that the proposed framework enables intelligent machine fault diagnosis without fault data. The diagnosis effect outperforms supervised, unsupervised and small-sample learning approaches and can be generalized to another load and speed with acceptable accuracy.

New identification of induction machine parameters with a meta-heuristic algorithm based on least squares method

PurposeThe great value of offline identification of machine parameters is when the machine manufacturer does not provide its parameters. Most machine control strategies require parameter values, and some circumstances in the industrial sector only require offline identification. This paper aims to present a new offline method for estimating induction motor parameters based on least squares and a salp swarm algorithm (SSA).Design/methodology/approachThe central concept is to use the classic least squares (LS) method to acquire the majority of induction machine (IM) constant parameters, followed by the SSA method to obtain all parameters and minimize errors.FindingsThe obtained results showed that the LS method gives good results in simulation based on the assumption that the measurements are noise-free. However, unlike in simulations, the LS method is unable to accurately identify the machine’s parameters during the experimental test. On the contrary, the SSA method proves higher efficiency and more precision for IM parameter estimation in both simulations and experimental tests.Originality/valueAfter performing a primary identification using the technique of least squares, the initial intention of this study was to apply the SSA for the purpose of identifying all of the machine’s parameters and minimizing errors. These two approaches use the same measurement from a simple running test of an IM, and they offer a quick processing time. Therefore, this combined offline strategy provides a reliable model based on the identified parameters.

Performance of Recursive Least Squares Algorithm Configurations for Online Parameter Identification of Induction Machines in an Automotive Environment

The recursive least squares algorithm for online parameter identification of induction machines has a high potential to serve as a basis for an innovative electric vehicle diagnosis concept. Commonly used for control parameter tuning, this approach is established in numerous industrial applications. However, in the automotive environment, special machine designs are used and highly dynamic operation takes place in a wide speed and load range. This results in fast transient parameter behavior, which is challenging in terms of online parameter identification. Therefore, the algorithm performance must be rated in our field of application with suitable dynamic test profiles. In this work, we compare several algorithm extensions to a novel recursive least squares algorithm with multiple variable forgetting factors for induction machines. The algorithms are analyzed regarding their handling of the associated transient parameter behavior. In addition, different identification model structures are considered to deal with the dynamic speed operation and the associated transient iron losses. Special attention is given to the real-time performance of the overall identification algorithms as this is a major requirement for implementation in automotive embedded systems. For validation, both, simulation and experimental results are presented, and associated configuration recommendations are provided.

A Hybrid Algorithm for Parameter Identification of Synchronous Reluctance Machines

In rail transit traction, synchronous reluctance machines (SynRMs) are potential alternatives to traditional AC motors due to their energy-saving and low-cost characteristics. However, the nonlinearities of SynRMs are more severe than permanent magnet synchronous motors (PMSM) and induction motors (IM), which means the characteristics of SynRMs are challenging to model accurately. The parameter identification directly influences the modeling of nonlinearity, while the existing algorithms tend to converge prematurely. To overcome this problem, in this paper, a hybrid optimizer combining the SCA with the SSO algorithm is proposed to obtain the parameters of SynRMs, and the proposed Sine-Cosine self-adaptive synergistic optimization (SCSSO) algorithm preserves the self-adaptive characteristic of SSO and the exploration ability of SCA. Comprehensive numerical simulation and experimental tests have fully demonstrated that the proposed method has obviously improved parameter identification accuracy and robustness. In the dq-axis flux linkage, the mismatch between reference and estimated data of proposed algorithm is below 1% and 6%, respectively. Moreover, the best d-axis RMSE of SCSSO is 50% of the well-known algorithm CLPSO and 25% of BLPSO and its performance has improved by two orders of magnitude compared to traditional simple algorithms. In the q-axis, the best RMSE is 10% of CLPSO and 50% of Rao-3 and Jaya. Moreover, the performance of the proposed algorithm has improved nearly 90 times compared to traditional simple algorithms.

Parameter Identification of Variable Flux Reluctance Machines Excited by Zero-Sequence Current Accounting for Inverter Nonlinearity

To identify the parameters of variable flux reluctance machines (VFRMs), the input voltage value is usually obtained from the reference voltage indirectly. However, the reference and actual voltages may be different due to the nonlinearity of the open winding inverter when using zero-sequence current, leading to the inaccuracy of parameter identification. To solve this problem, this paper proposes an equivalent nonlinearity voltage error model of the open winding inverter to compensate for the input voltage of VFRM during the online parameter identification. Since in the proposed method the phase current direction is not required, the calculation time can be reduced. Moreover, in the developed parameter identification model of VFRM the derived equivalent nonlinear voltage error is used to correct the input of the identification model so as to improve the accuracy of parameters. Finally, the experimental results on the prototype VFRM are presented for verification.

Decoupled Estimation Scheme for PMSMs Toward Accurate Inductance Modeling

Parameter identification of permanent magnet synchronous machines (PMSMs) is critical toward building an accurate machine model, achieving precise control, and improving operational reliability. This article proposes a decoupled estimation scheme for interior PMSM with consideration of magnetic saturation as nonlinear functions of current to improve the inductance estimation accuracy. An improved machine model considering magnetic saturation and cross-coupling effects by utilizing a quadratic model in inductance calculation is developed. Furthermore, the decoupled estimation scheme with considering stator resistance and iron loss resistance can effectively reduce the interaction in the process of parameter identification, and the least square algorithm is employed to improve the computational efficiency. Moreover, this proposed method is especially dedicated to the modern testing solutions that require complex hardware and extensive machine operation while being noninvasive by nature.

Two Novel Approaches for Identification of Synchronous Machine Parameters From Short-Circuit Current Waveform

In this article, two novel approaches for identifying the synchronous generator (SG) parameters are presented. The proposed approaches use an experimentally obtained armature current during the short-circuit test as input data. Both approaches are simple, cost-effective, and practical since they do not require any additional equipment for signal generation nor applying any of the advanced control techniques. The first approach relies on fitting the analytical total armature current waveform with the experimental one, while the second approach relies on matching the observed waveform with an analytical waveform of asymmetrical and alternating components. Instead of traditional graphical methods typically used in standardized procedures, this article also proposes a novel hybrid metaheuristic algorithm to identify the SG's parameters. The experimental verification of the proposed methods is carried out on 100 MVA SG at the Bajina Basta hydropower plant in Serbia.

Full Synchronous Machine Parameters Identification Based on Field and Armature Current During the Short-Circuit

Full Synchronous Machine Parameters Identification Based on Field and Armature Current During the Short-Circuit

Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources.

Parameter identification of permanent magnet synchronous machines (PMSMs) represents a well-established research area. However, parameter estimation of multiple running machines in large-scale applications has not yet been investigated. In this context, a flexible and automated approach is required to minimize complexity, costs, and human interventions without requiring machine information. This paper proposes a novel identification strategy for surface PMSMs (SPMSMs), highly suitable for large-scale systems. A novel multistep approach using measurement data at different operating conditions of the SPMSM is proposed to perform the parameter identification without requiring signal injection, extra sensors, machine information, and human interventions. Thus, the proposed method overcomes numerous issues of the existing parameter identification schemes. An IoT/cloud architecture is designed to implement the proposed multistep procedure and massively perform SPMSM parameter identifications. Finally, hardware-in-the-loop results show the effectiveness of the proposed approach.

Permanent Magnet Synchronous Machine Torque Estimation Using Low Cost Hall-Effect Sensors

Torque measurement/estimation in permanent magnet synchronous machines (PMSMs) is required in a large variety of applications. Direct torque measurement by means of torque transducers is typically limited to laboratory applications due to cost, reliability, and room concerns. Alternatively, torque can be estimated. Torque estimation methods require accurate knowledge of machine parameters. Injection of a high frequency (HF) signal in the stator via inverter has been shown to be a viable option for online machine parameters identification in PMSMs. However, this introduces concerns on the performance of the machine due to the noise, vibration, and additional losses produced by the HF signal. This article proposes a torque estimation method for PMSMs using low cost Hall-effect sensors. The method is based on the dependency of the leakage flux complex vector due to interaction between permanent magnet flux and stator coil flux and does not interfere with the drive operation of the machine.

Self-Adaptive Synergistic Optimization for Parameters Extraction of Synchronous Reluctance Machine Nonlinear Magnetic Model

For mechanism analysis and high-performance control of synchronous reluctance machine (SynRM), accurate and reliable parameter identification of nonlinear magnetic model is always required. However, the accuracy and robustness of traditional heuristic algorithms are restricted by incomplete individual performance evaluation and single population evolution mechanism. In this paper, we propose a self-adaptive synergistic optimization (SSO) algorithm for extracting the parameters of the model. A novel synergistic-performance evaluation is first established to classify candidates automatically. Then, a self-organized mechanism is proposed to select optimal evolution strategies designed for classified candidate solutions. Around the current best candidate, the exploration is guaranteed in priority. Meanwhile, a self-adaptive mechanism is introduced to select other candidates to construct more promising evolutionary direction. Thus, achieving a good balance between exploration and exploitation. The parameter estimation performance of SSO algorithm is evaluated through standard datasets of SynRM magnetic model obtained by the finite element analysis. Comprehensive experiment results demonstrate the competitiveness and effectiveness of the proposed SSO algorithm compared with other algorithms, especially in terms of the accuracy and robustness. According to these superiorities, it can be concluded that the proposed algorithms are promising parameter identification methods for SynRM nonlinear magnetic model.

Modal parameter identification of time-varying systems via wavelet-based frequency response function

Modal parameter identification of systems, structures and machines with variable physical parameters is an important task for their damage diagnosis, maintenance and repair, and life cycle management, especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step, i.e., estimated wavelet-based frequency response function (FRF) and least-squares iterative algorithm, modeling approach in order to determine time-varying vibration modes based on Gaussian white noise input excitation. The time-varying wavelet-based FRF is estimated based on output noise estimation model $$H_1$$ ; the second step identifies time-varying modal parameters based on estimated wavelet-based FRF with the use of least-squares iterative algorithm. Estimated wavelet-based FRF can reveal the dynamic characteristics of the system correctly similar to the theoretical FRF by single degree of freedom (SDOF). The combined method is demonstrated using system identification analysis based on the simulated stiffness-varying and mass-varying multiple-degree-of-freedom (MDOF) system subjected to random Gaussian white noise excitation. The results show that the proposed combined method accurately identifies modal parameter of the time-invariant analyzed structure, and correctly captures modal parameter of the time-varying analyzed structure. The modal frequency and shape agree with theoretical results well. The analysis results also indicate that the proposed combined method is sensitive to the position of input excitation. The estimation accuracy wavelet-based FRF can be improved by selecting the average number of times and wavelet parameters.

Two-Step Continuous-Control Set Model Predictive Current Control Strategy for SPMSM Sensorless Drives

Integration of model predictive current control (MPCC) and the existing position estimation method is an attractive and promising strategy for surface-mounted permanent magnet synchronous machines (SPMSMs) sensorless drives. However, its system control performance is severely weakened by the filter delay and position estimation errors in the position estimation process because the conventional MPCC requires accurate rotor position information. In order to effectively resolve these problems, a two-step continuous-control-set MPCC (TS-CCS-MPCC) strategy is proposed in this paper. By adopting the two-step prediction to complete the machine mathematical model, the predictive currents can be obtained without the estimated rotor position. Meanwhile, a new proportional cost function is developed to obtain the optimal voltage vector. With the proposed control strategy, the necessary position estimation process in the conventional MPCC can be removed, and the MPCC-based sensorless control can be achieved rapidly and steadily, which not only simplifies the whole implementation process but also dramatically improves the system dynamic performance. Besides, the one-beat delay compensation and machine parameter identification are deduced to enhance the control performance of the proposed scheme. Finally, the experiments validate that the proposed sensorless control scheme can achieve excellent control performance for both steady-state and dynamic operations.

SPMSMs Sensorless Torque Estimation Using High-Frequency Signal Injection

Torque measurement/estimation in surface permanent magnet synchronous machines (SPMSMs) is needed in many applications. Torque measurement systems are expensive, require extra room, add weight, can introduce resonances into the system, and their performance can be compromised due to electromagnetic interference. Alternatively, torque can be estimated, accurate knowledge of machine parameters being critical in this case. Injection of a high-frequency (HF) signal in the stator via an inverter has been shown to be a viable option for online identification of machine parameters identification required by torque estimation methods for interior PMSMs. However, the reported methods require the use of two pulsating HF currents, which introduces concerns both on the adverse effects on machine's performance (additional noise, vibration, losses, etc.) as well as to the additional computational burden, as HF current controllers are required. This article proposes a torque estimation method for SPMSMs using a single HF signal; furthermore, a HF voltage signal can be used, avoiding the use of HF current controllers, and therefore simplifying the implementation.

Offline Parameter Self-Learning Method for General-Purpose PMSM Drives With Estimation Error Compensation

Offline parameter identification of permanent magnet synchronous machines (PMSMs) is of great importance for general-purpose drives with sensorless control. This paper proposes an amplitude-auto-adjusting signal injection (ASI) method for the parameter self-learning of PMSMs at standstill considering inverter nonlinearities and the digital time-delay effect. The ASI method achieves the inductance identification process under various dq -axis currents and at the same time prevents the unexpected rotor rotation during the self-commissioning process. For the test PMSM, the spatial inductance maps of dq -axes and abc -phases concerning the magnetic saturation and cross-coupling effects are identified along with the stator resistance. To enhance the estimation accuracy, an error model of the inverter nonlinearities in dq -axes is established, and a compensation method independent of inverter parameters is proposed based on the Hermite interpolation. In addition, the influence of the digital time-delay effect is analyzed and compensated based on the transient model of the circuits. The effectiveness of the proposed parameter self-learning scheme is confirmed on a 2.2-kW PMSM drive. The accuracy of the experimental results is validated by finite element analysis on the test machine.

A review of biomaterials scaffold fabrication in additive manufacturing for tissue engineering

The current developments in three-dimensional printing also referred as “additive manufacturing” have transformed the scenarios for modern manufacturing and engineering design processes which show greatest advantages for the fabrication of complex structures such as scaffold for tissue engineering. This review aims to introduce additive manufacturing techniques in tissue engineering, types of biomaterials used in scaffold fabrication, as well as in vitro and in vivo evaluations. Biomaterials and fabrication methods could critically affect the outcomes of scaffold mechanical properties, design architectures, and cell proliferations. In addition, an ideal scaffold aids the efficiency of cell proliferation and allows the movements of cell nutrient inside the human body with their specific material properties. This article provides comprehensive review that covers broad range of all the biomaterial types using various additive manufacturing technologies. The data were extracted from 2008 to 2018 mostly from Google Scholar, ScienceDirect, and Scopus using keywords such as “Additive Manufacturing,” “3D Printing,” “Tissue Engineering,” “Biomaterial” and “Scaffold.” A 10 years research in this area was found to be mostly focused toward obtaining an ideal scaffold by investigating the fabrication strategies, biomaterials compatibility, scaffold design effectiveness through computer-aided design modeling, and optimum printing machine parameters identification. As a conclusion, this ideal scaffold fabrication can be obtained with the combination of different materials that could enhance the material properties which performed well in optimum additive manufacturing condition. Yet, there are still many challenges from the printing methods, bioprinting and cell culturing that needs to be discovered and investigated in the future.

Parameter Identification of Current–Force Factor and Torque Constant in Single-Drive Bearingless Motors With Back EMF

One-axis actively positioned bearingless motors are studied for industry applications such as pumps and cooling fans. In particular, single-drive bearingless motors have a great advantage of having a simple regulation system. Only one three-phase inverter is required to generate both torque and a magnetic suspension force by the q- and d-axis currents, respectively. Therefore, their voltage equation is unique. In addition to the rotational speed induced voltage in the q-axis, the translation speed voltage is generated in the d-axis. Therefore, both the current–force factor and the torque constant can be identified from the back electromotive force (EMF). In this paper, a novel simplified identification method of the current–force factor is proposed that does not require the measurement of the active axial force and current. Furthermore, it is experimentally verified that this method is effective in the identification of machine parameters.

Parameter estimation of synchronous machines considering field voltage variation during the sudden short-circuit test

This work examines the influence of field voltage variation during a sudden short-circuit test and its direct impact on the parameter identification of synchronous machines. The test standards establish that field voltage must be kept constant during the short-circuit test. However, due to the presence of impedances in the voltage supply, control of the excitation system along with many other factors, field voltage may vary during this test. This work proposes a method to recover the machine parameters even when high amplitude field voltage variations are presented during the sudden short-circuit test. In addition, an algorithm capable of defining maximum field voltage variations along with its correspondent duration in order to respect a certain parameter estimation error is proposed. Finally, the models developed in this paper are investigated using data from a 140 MVA synchronous machine.

Standstill Frequency Response Test and Validation of a Large Hydrogenerator

This paper intends to contribute to the revision process of the IEEE Standard 115 by demonstrating the applicability of the standstill frequency response (SSFR) test on large salient pole hydrogenerators. The presented SSFR tests are carried out on a 55.6-MVA salient pole machine with laminated rotor, non-continuous damper windings, and a nonintegral slot number. The IEEE-115 SSFR test procedure is applied with special care to rotor positioning as well as accurate data acquisition in the low-frequency range. The maximum likelihood estimation method is utilized for machine parameter identification from the SSFR tests. Obtained parameters are compared with design values in addition to the ones obtained using traditional “sudden no-load three-phase short-circuit,” Dalton–Cameron and “open stator d-axis transient time constant” methods. The accuracy of parameters is also confirmed by comparing the measured three-phase short-circuit current waveforms with the ones obtained by simulating the SSFR-based machine models in an electromagnetic transient -type software. Unlike previous SSFR test cases on large salient pole hydrogenerators, accurate results are obtained.

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.