A Fault Diagnosis Method for Automaton Based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition

نویسندگان

  • F. Wang Department of Artillery Engineering, Army Engineering University, He Ping Road, Shijiazhuang China
  • L. Fang Department of Artillery Engineering, Army Engineering University, He Ping Road, Shijiazhuang China
چکیده مقاله:

In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method in the signal separation, using the morphological difference of the components in the automatic vibration signal, different sparse dictionaries were constructed to separate the components, eliminates the noise components and extracted the effective fault characteristic component, the extracted impact components are decomposed by EEMD and the energy feature of each IMF component is calculated as the fault features, then put the fault features into SVM (Support Vector Machine) and identify the faults. Through the construction simulation example and the typical fault simulation test of automatic machine, it showed that the morphological component analysis method had better noise reduction and signal separation effect. Compared with the traditional EEMD method, the feature extraction method based on the MCA-EEMD can distinguish automaton fault types more effectively.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Fault Diagnosis Method for Automaton based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition

In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...

متن کامل

Improved Ensemble Empirical Mode Decomposition for Rolling Bearing Fault Diagnosis

Rolling bearing is an important part in mechanical system and faults occur frequently with vibration noise. Empirical mode decomposition (EMD) is a tool for nonlinear and non-stationary signals analysis. However, the major drawbacks of EMD are mode mixing problem, ensemble empirical mode decomposition (EEMD) provides a new tool for signal analysis, and it is an improved technique of EMD. In ord...

متن کامل

A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition

A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals' separation, the pre...

متن کامل

Sensor Fault Diagnosis Based on Ensemble Empirical Mode Decomposition and Optimized Least Squares Support Vector Machine

A fault diagnosis method for sensor fault based on ensemble empirical mode decomposition (EEMD) energy entropy and optimized structural parameters least squares support vector machine (LSSVM) is put forward in this paper. Firstly, the original output fault signals are pretreatment with EEMD, and then the EEMD energy entropy is extracted as the fault feature vector. Then the radial basis functio...

متن کامل

Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition

The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decom...

متن کامل

Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the p...

متن کامل

منابع من

با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ذخیره در منابع من قبلا به منابع من ذحیره شده

{@ msg_add @}


عنوان ژورنال

دوره 32  شماره 6

صفحات  877- 883

تاریخ انتشار 2019-06-01

با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.

میزبانی شده توسط پلتفرم ابری doprax.com

copyright © 2015-2023