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Author

Jiwen Zhao

Bio: Jiwen Zhao is an academic researcher from Anhui University. The author has contributed to research in topics: Synchronous motor & Materials science. The author has an hindex of 14, co-authored 36 publications receiving 528 citations.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
Zhenbao Pan1, Fei Dong1, Jiwen Zhao1, Lijun Wang1, Hui Wang1, Yinyi Feng1 
TL;DR: A new control method to suppress current harmonics for permanent magnet synchronous linear motor (PMSLM) that is applied in the miniature microsecond laser cutting system and can reduce the overshoot and thrust ripple as well is presented.
Abstract: This paper presents a new control method to suppress current harmonics for permanent magnet synchronous linear motor (PMSLM) that is applied in the miniature microsecond laser cutting system. In the control method, the resonant–two-degree-of-freedom (R–2DOF) proportional–integral–derivative (PID) controller is proposed by combining a resonant controller and a two-degree-of-freedom (2DOF) PID controller. The current harmonic components are first analyzed. The resonant controller is subsequently added to the current loop in parallel to the traditional PI controller to suppress the current harmonic components. However, with the current harmonics suppression, the resonant controller can result in the overshoot in the current loop response. The 2DOF PID controller is adopted to reduce the overshoot. Thus, an R–2DOF PID controller is developed by combining the resonant controller and 2DOF PID controller. Meanwhile, the stability of the proposed controller is analyzed. Compared with the traditional PID controller and the Kalman filter, the proposed controller not only can suppress the current harmonics but can reduce the overshoot and thrust ripple as well. Finally, the simulation and experimental comparison results confirm the validity of the proposed control algorithm.

100 citations

Journal ArticleDOI
Juncai Song1, Fei Dong1, Jiwen Zhao1, Hui Wang1, Zhongyan He1, Lijun Wang1 
TL;DR: In this paper, an efficient multiobjective design optimization method for a permanent magnet synchronous linear motors (PMSLMs) is proposed to achieve optimal performances as indicated by high average thrust, low thrust ripple, and low total harmonic distortion at different running speeds.
Abstract: This paper focuses on the multiobjective design optimization of the permanent magnet synchronous linear motors (PMSLMs), which are applied to a high-precision laser engraving machine. A novel efficient multiobjective design optimization method for a PMSLM is proposed to achieve optimal performances as indicated by high average thrust, low thrust ripple, and low total harmonic distortion at different running speeds. First, based on the finite-element analysis (FEA) data, a regression machine learning algorithm, called an extreme learning machine (ELM), is introduced to solve the calculation modeling problem by mapping out the nonlinear and complex relationship between input structural factors and output motor performances. Comparative simulation experiments conducted using the traditional analytical modeling method and another machine learning modeling method, i.e., support vector machine, confirm the superiority of the ELM. Then, a new bionic intelligent optimization algorithm, called the gray wolf optimizer algorithm, is used to search the best optimization performances and structural parameters by performing iteration optimization calculation for multiobjective functions. Finally, FEA and prototype motor experiments prove the effectiveness and validity of the proposed method.

77 citations

Journal ArticleDOI
Siliang Lu1, Peng Zhou1, Xiaoxian Wang1, Yongbin Liu1, Fang Liu1, Jiwen Zhao1 
TL;DR: A new method for motor bearings condition monitoring and fault diagnosis using the undersampled vibration signals acquired from a WSN, which is a fusion of the kurtogram, analog domain bandpass filtering, bandpass sampling, and demodulated resonance technique is investigated.

69 citations

Journal ArticleDOI
TL;DR: In this article, a fast and online OA (FOOA) method is proposed to realize variable-speed PMSM bearing fault diagnosis in an embedded system for online fault diagnosis, which consists of two algorithms: rotating phase information is extracted from the sinusoidal current of the PMSM, and a series of equal phase sampling pulses are generated.

64 citations

Journal ArticleDOI
TL;DR: In this article, the authors focus on optimisation design of permanent magnet linear synchronous motors that are applied in laser engraving machines with no cutting force and propose the Taguchi method based on orthogonal array to optimise the thrust and thrust ripple.
Abstract: This study focuses on optimisation design of permanent magnet linear synchronous motors that are applied in laser engraving machines with no cutting force. Traditional analytical optimisation method based on magnetic field with particle swarm optimisation algorithm was introduced to obtain the best combination of motor structure parameters. By contrast, the novel optimisation design method - Taguchi method based on orthogonal array was proposed to optimise the thrust and thrust ripple. After the design of experiments using finite-element analysis, the relative importance of each design parameter was estimated in detail. Experimental results of prototype can certify the superiority and validity of Taguchi optimisation method.

60 citations


Cited by
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Journal ArticleDOI
Yaguo Lei1, Naipeng Li1, Liang Guo1, Ningbo Li1, Tao Yan1, Jing Lin1 
TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.

1,116 citations

01 Jan 2004
TL;DR: A new algorithm for manifold learning and nonlinear dimensionality reduction is presented based on a set of unorganized da-ta points sampled with noise from a parameterized manifold, and the local geometry of the manifold is learned by constructing an approxi-mation for the tangent space at each point.
Abstract: We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized da-ta points sampled with noise from a parameterized manifold, the local geometry of the manifold is learned by constructing an approxi-mation for the tangent space at each point, and those tangent spaces are then aligned to give the global coordinates of the data pointswith respect to the underlying manifold. We also present an error analysis of our algorithm showing that reconstruction errors can bequite small in some cases. We illustrate our algorithm using curves and surfaces both in 2D/3D Euclidean spaces and higher dimension-al Euclidean spaces. We also address several theoretical and algorithmic issues for further research and improvements.

670 citations

Journal ArticleDOI
TL;DR: The proposed domain adaptation method offers a new and promising tool for intelligent fault diagnosis and can be efficiently extracted in this way, and the cross-domain testing performance can be significantly improved.

283 citations

Journal ArticleDOI
TL;DR: This study is committed to providing a comprehensive review of SR from history to state-of-the-art methods and finally to research prospects, along with the applications in rotating machine fault detection.

252 citations

Journal ArticleDOI
TL;DR: The proposed methods had good results for diagnosis of bearing, stator and rotor faults of the single-phase induction motor and can find applications for fault diagnosis of other types of rotating machines.

247 citations