A new fault feature extraction method of rotating machinery based on finite sample function

2020 ◽  
pp. 095745652094827
Author(s):  
Feng Miao ◽  
Rongzhen Zhao
Keyword(s):  
Feature Extraction ◽  
Separation Performance ◽  
Finite Sample ◽  
Finite Support ◽  
Vibration Signals ◽  
Score Functions ◽  
Feature Extraction Method ◽  
Fault Features ◽  
Simulation Results ◽  
Fault Feature Extraction

Feature extraction plays a crucial role in the diagnosis of rotating machinery’s faults. In order to separate different fault vibration signals from measured mixtures and diagnose the fault features of the machine effectively according to the separated signals, a blind source separation (BSS) method using kernel function based on finite support samples was proposed. The method is stronger adaptability to the score functions estimated according to finite support observed signal samples. The simulation results prove that the proposed BSS algorithm is able to separate mixed signals that contain both sub-Gaussian and super-Gaussian sources. It is shown that the algorithm has better separation performance when compared with other BSS ones. The results of an experiment under the rotor’s composite fault states with rub-impact fault and unbalance fault show that this method has higher efficiency and accuracy.

Feature Extraction of Rolling Bearing Based on LMD Energy Feature

2017 ◽  
Vol 868 ◽  
pp. 363-368
Author(s):  
Bang Sheng Xing ◽  
Le Xu
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Small Samples ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Fault Features ◽  
Local Mean ◽  
Energy Feature

For the situation that it is difficult to diagnose rolling bearings fault effectively for small samples, so it proposes a feature extraction method of rolling bearing based on local mean decomposition (LMD) energy feature. Due to the frequency domain distribution of vibration signals will change when different faults occur in rolling bearings, so it can use LMD energy feature method to extract the fault features of rolling bearings. The instances analysis and extracted results show that the LMD energy feature can extract the vibration signal fault feature of rolling bearings effectively.

Fault Feature Extraction of High-Speed Automaton Based on Motion Morphology Decomposition

2013 ◽  
Vol 347-350 ◽  
pp. 224-227
Author(s):  
Ai Yu Wang ◽  
Hong Xia Pan ◽  
Hui Ling Liu
Keyword(s):  
Feature Extraction ◽  
High Speed ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Characteristic Parameters ◽  
Feature Extraction Method ◽  
Fault Features ◽  
Fault State ◽  
Fault Feature Extraction ◽  
Fault Characteristic

In order to obtain the characteristic parameters reflecting fault state of high-speed automaton (HSA), the fault feature extraction method based on motion morphology decomposition and wavelet packet transform (WPT) was presented. According to the movement law of the automaton, the vibration signal generated in three bursts of fire was decomposed into three separate signals, then the response signal in each shooting is a separate signal. Then using WPT to respectively extract wavelet packet energy from three separate signals as the fault characteristic parameters of HSA. By the example, the results show that the extracted fault features can well reflect the working conditions of automaton. Thus the proposed method could be used to extract the fault feature of automaton for monitoring the condition and diagnosing the fault of automaton.

scholarly journals Automatic Bearing Fault Feature Extraction Method via PFDIC and DBAS

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhiqiang Liao ◽  
Xuewei Song ◽  
Baozhu Jia ◽  
Peng Chen
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Extraction Method ◽  
Singular Values ◽  
Singular Value ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Fault Features ◽  
Value Decomposition ◽  
Fault Feature Extraction

Determining the embedded dimension of a singular value decomposition Hankel matrix and selecting the singular values representing the intrinsic information of fault features are challenging tasks. Given these issues, this work presents a singular value decomposition-based automatic fault feature extraction method that uses the probability-frequency density information criterion (PFDIC) and dual beetle antennae search (DBAS). DBAS employs embedded dimension and singular values as dynamic variables and PFDIC as a two-stage objective to optimize the best parameters. The optimization results work for singular value decomposition for bearing fault feature extraction. The extracted fault signals combined with envelope demodulation can efficiently diagnose bearing faults. The superiority and applicability of the proposed method are validated by simulation signals, engineering signals, and comparison experiments. Results demonstrate that the proposed method can sufficiently extract fault features and accurately diagnose faults.

scholarly journals The Extraction Method of Gearbox Compound Fault Features Based on EEMD and Cloud Model

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ling Zhao ◽  
Jiaxing Gong ◽  
Hu Chong
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Cloud Model ◽  
Multiple Faults ◽  
Feature Extraction Method ◽  
Fault Features ◽  
Measurement Principle ◽  
Feature Dimension ◽  
Fault Feature Extraction ◽  
Fault Characteristic

When a compound fault occurs, the randomness and ambiguity of the gearbox will cause uncertainty in the collected signal and reduce the accuracy of signal feature extraction. To improve accuracy, this research proposes a gearbox compound fault feature extraction method, which uses the inverse cloud model to obtain the signal feature value. First, EEMD is used to decompose the collected vibration signals of gearbox faults in normal and fault states. Then, the mutual information method is used to select the sensitive eigenmode function that can reflect the characteristics of the signal. Subsequently, the inverse cloud generator is used to extract cloud digital features and construct sample feature sets. On this basis, the concept of synthetic cloud is introduced, and the cloud-based distance measurement principle is used to synthesize new clouds, reduce the feature dimension, and extract relevant features. Finally, a simulation experiment on a rotating machinery unit with a certain type of equipment verifies that the proposed method can effectively extract the feature of gearbox multiple faults with less feature dimension. And comparing with the feature set extracted by the single cloud model, the results show that the method can better represent the fault characteristic information of the signal.

scholarly journals Instantaneous Amplitude-Frequency Feature Extraction for Rotor Fault Based on BEMD and Hilbert Transform

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Chuanjin Huang ◽  
Haijun Song ◽  
Wenping Lei ◽  
Zhanya Niu ◽  
Yajun Meng
Keyword(s):  
Feature Extraction ◽  
Hilbert Transform ◽  
Vibration Signal ◽  
Three Dimensions ◽  
Intrinsic Mode Functions ◽  
Instantaneous Amplitude ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Fault Features ◽  
The Hilbert Transform

The vibration signals propagating in different directions from rotating machines can contain a variety of characteristic information. A novel feature extraction method based on bivariate empirical mode decomposition (BEMD) for rotor is proposed to comprehensively extract the fault features. In this work, the number of signal projection directions is determined through simulation, and the energy end condition based on the energy threshold is increased using BEMD to enhance the decomposition quality. Mixed vibration signals are generated along two orthogonal directions. Then, the acquired vibration signal can be decomposed into several intrinsic mode functions (IMFs) at the rotational speed using the BEMD method. Furthermore, the instantaneous frequency and instantaneous amplitude of the real signals and the imaginary part of the IMF signals are obtained using the Hilbert transform. The fault features along two and three dimensions can be investigated, providing more comprehensive information to aid in the fault diagnosis of rotor. Experimental results on oil film oscillation, the oil whirl, the bistability of the rotor, and looseness and rotor rubbing composite fault indicate the effectiveness of the proposed method.

Research of Fault Feature Extraction Based on High Order Cyclic Statistics for Reciprocating Compressor Gas Valves

2010 ◽  
Vol 44-47 ◽  
pp. 2094-2098
Author(s):  
Shu Lin Liu ◽  
You Fu Tang ◽  
Ji Cheng Liu ◽  
Ying Hui Liu
Keyword(s):  
Feature Extraction ◽  
New Method ◽  
Reciprocating Compressor ◽  
Third Order ◽  
Vibration Signals ◽  
The Third ◽  
Signal Features ◽  
Fault Features ◽  
Cyclic Statistics ◽  
Fault Feature Extraction

. This paper proposes an approach of fault feature extraction for reciprocating compressor gas valves based on theory of cyclic statistics. First, the strength and weakness of the third-order cyclic statistics in extracting signal features are investigated by simulation signals. Since vibration signals for reciprocating compressor gas valves are of typical cyclic stationary, a new method of fault feature extraction is then proposed based on the simulation results. The method utilizes the cyclic bi-spectrum to extract fault features for the corresponding frequencies. The results show that the cyclic bi-spectrum characteristics for typical faults of gas valves are apparently different, and that the typical faults of reciprocating compressor gas valves can be diagnosed exactly. So the new method proposed in this paper is effective and feasible.

A fractal-dimension-based envelope demodulation for rolling element bearing fault feature extraction from vibration signals

2016 ◽  
Vol 230 (18) ◽  
pp. 3194-3211 ◽  
Author(s):  
Juanjuan Shi ◽  
Ming Liang
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
Search Algorithm ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Condition Monitoring ◽  
Interference Frequency ◽  
Fault Feature Extraction

Vibration analysis has been extensively used as an effective tool for bearing condition monitoring. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault feature (i.e. fault-induced impulses), periodic interferences from other mechanical/electrical components, and background noise. The incipient impulses which excite as well as modulate the resonance frequency of the system are easily masked by compounded effects of periodic interferences and noise, making it challenging to do a reliable fault diagnosis. As such, this paper proposes an envelope demodulation method termed short time fractal dimension (STFD) transform for fault feature extraction from such vibration signal mixture. STFD transform calculation related issues are first addressed. Then, by STFD, the original signal can be quickly transformed into a STFD representation, where the envelope of fault-induced impulses becomes more pronounced whereas interferences are partly weakened due to their morphological appearance differences. It has been found that the lower the interference frequency, the less effect the interference has on STFD representations. When interference frequency keeps increasing, more effects on STFD representations will be resulted. Such effects can be reduced by the proposed kurtosis-based peak search algorithm (KPSA). Therefore, bearing fault signature is kept and interferences are further weakened in the STFD-KPSA representation. The proposed method has been favourably compared with two widely used enveloping methods, i.e. multi-morphological analysis and energy operator, in terms of extracting impulse envelopes from vibration signals obscured by multiple interferences. Its performance has also been examined using both simulated and experimental data.

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