Broken rotor bars detection via Park's vector approach based on ANFIS

Many attempts have been made on fault diagnosis of induction motors based on frequency and time domain analysis of stator current. In this paper, first the Park's vector transformation and frequency analysis for fault detection of induction motors are introduced. Then a smart approach using Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed. This approach uses the time domain features derived from the Park's vector transformation of stator current. By the proposed method, a partial break including 5 mm crack on a bar, one broken bar and two broken bars using experimental data are investigated. It will be shown that features derived from Park's vector compared to features obtained fro…

A modified observer-based prediction approach for industrial applications

The prediction of key variables has great significance to monitor the running status of industrial systems. In this paper, a novel data-driven design of variable predictor is proposed. The basic idea is the realization of prediction observer, which is modified from the observer-based fault diagnose method. Different from the standard data-driven approaches, the proposed scheme is adopted for the dynamic systems due to the superior tracking ability of output observer. Additionally, by introducing an extra design freedom and the estimation of measured value, it can also be used for the case that the key variable is not on-line measurable. Finally, the proposed prediction scheme is applied to …

EEMD based analysis of vehicle crash responses

The vehicle crash is a complex process with nonlinear large deformation of structures. The analysis of the crash process is one of the challenges for all vehicle safety researchers. In this paper, the Ensemble Empirical Mode Decomposition (EEMD) method is applied in the analysis of crash responses in order to achieve some meaningful results. With the help of EEMD, the crash responses are decomposed into a trend signal and some high frequency fluctuations. By studying the load path of vehicle design, each component is corresponding to the structure of vehicle body. Consequently, some parameters of vehicle crash model can be identified. A frontal crash of Toyota Yaris is employed for demonstr…

Data-based modeling and estimation of vehicle crash processes in frontal fixed-barrier crashes

Abstract As a complex process, vehicle crash is challenging to be described and estimated mathematically. Although different mathematical models are developed, it is still difficult to balance the complexity of models and the performance of estimation. The aim of this work is to propose a novel scheme to model and estimate the processes of vehicle-barrier frontal crashes. In this work, a piecewise model structure is predefined to represent the accelerations of vehicle in frontal crashes. Each segment in the model is corresponding to the energy absorbing component in the crashworthiness structure. With the help of Ensemble Empirical Mode Decomposition (EEMD), a robust scheme is proposed for …

Analysis of the Relationship between Energy Absorbing Components and Vehicle Crash Response

An EEMD Aided Comparison of Time Histories and Its Application in Vehicle Safety

In the context of signal processing, the comparison of time histories is required for different purposes, especially for the model validation of vehicle safety. Most of the existing metrics focus on the mathematical value only. Therefore, they suffer the measuring errors, disturbance, and uncertainties and can hardly achieve a stable result with a clear physical interpretation. This paper proposes a novel scheme of time histories comparison to be used in vehicle safety analysis. More specifically, each signal for comparison is decomposed into a trend signal and several intrinsic mode functions (IMFs) by ensemble empirical mode decomposition. The trend signals reflect the general variation a…

A subspace based fault diagnose method and its application on mechatronics systems

The mechatronics systems are widely used in modern society. This paper presents a novel data-driven scheme which can be used for fault diagnose of mechatronics systems. The proposed method is based on the subspace identification of parity vector. By constructing the output observer, critical variables can be acquired by soft sensors. This makes the fault diagnoses free from the limitation of online measurement. A diagnose observer is designed directly from the parity vector. Finally, the proposed scheme is tested by the Simulink benchmark of vehicle suspension and shows its good performance.