Yu Guo

Order Tracking Based on Robust Peak Search Instantaneous Frequency Estimation

Order tracking plays an important role in non-stationary vibration analysis of rotating machinery, especially to run-up or coast down. An instantaneous frequency estimation (IFE) based order tracking of rotating machinery is introduced. In which, a peak search algorithms of spectrogram of time-frequency analysis is employed to obtain IFE of vibrations. An improvement to peak search is proposed, which can avoid strong non-order components or noises disturbing to the peak search work. Compared with traditional methods of order tracking, IFE based order tracking is simplified in application and only software depended. Testing testify the validity of the method. This method is an effective supplement to traditional methods, and the application in condition monitoring and diagnosis of rotating machinery is imaginable.

Vibration Analysis of Rotor System by Combined Adaptive Time-Frequency Analysis and Independent Component Analysis

Traditional time-frequency analysis methods such as short-time Fourier transform (STFT) and Wigner-Ville distribution (WVD) cannot always work effectively for the complex rotor systems where the multiple faults are involved. A noise cancellation method for the rotor faults detection is proposed in this paper by combining the independent component analysis (ICA) scheme and the adaptive time-frequency analysis (ATFA) approach. In the proposed method, the raw picked data are first separated into different independent components (ICs) via the ICA according to the different vibration sources. Then the ICs are processed by the ATFA to obtain a clear vibration character for the fault diagnosis. Experiments on a rotor system with hybrid fault of the rotor imbalance and the bolt looseness are introduced to verify the feasibility and validity of the proposed scheme.

Double Impulses Extraction of Faulty Rolling Element Bearing Based on EEMD and Complex Morlet Wavelet

A method for the double impulses extraction of faulty rolling element bearing (REB) is proposed in this paper. In the proposed approach, the (Ensemble Empirical Mode Decomposition) EEMD are employed for filtering the random noise and high-frequency continuous noise without any mode mixing. Then, the extracted signal is filtered with complex Morlet wavelet, which enhanced the double impulses greatly. As a result, clear double impulses can be obtained through the envelope. The effectiveness of the approach is demonstrated by the simulation study.

Study on Remote Condition Monitoring and Fault Diagnosis System for Rotating Machinery Based on LabVIEW

In order to suit the demand of monitoring and fault diagnosis of modern small and medium machinery devices better, this paper discusses the development of machinery condition monitoring and fault diagnosis system of good universality and strong expansibility using LabVIEW. Mainly illuminates vibration signal, temperature signal and electric current signal acquisition module using NI data acquisition hardware; signal analysis module in time domain, frequency domain and joint time–frequency domain using signal processing technology. DataSocket, database and fuzzy diagnosis technique have been utilized enabling this system to monitor and diagnose machinery fault remotely.

Development of PCG Monitor Based on Virtual Instrument Technology

By utilized VISA functions provided by LabVIEW, we can easily and successfully realize the serial communication between cardiechema senor and monitoring system, as well as collected data written into and read from Access through database functions. The perfect combination between the internet technology and the virtual instrument has provided a promising platform for the realization of the virtual instrument networking. This paper mainly introduces the construction of phonocardiogram monitoring system which has some functions such as real-time monitoring, data storage, signal preprocessing, playback, characteristic signal extraction, remote monitoring and so on.

Feature Extraction of Faulty Rolling Element Bearing Based on Time Synchronous Average and Cepstrum Edit

The cepstrum edit scheme for the vibration feature extraction of the faulty rolling element bearing (REB) is studied in this paper. By combined the time synchronous average (TSA) and the real cepstrum to localize and edit the cepstral lines of the original vibration, the unwanted discrete frequency components can be removed. Then, a corresponding inverse procedure is designed, in which the edited cepstrum and the original phase spectrum are employed to reconstruct the edited vibration for the REB feature extraction. Simulation verified the scheme positively.

Rolling Element Bearing Diagnosis Based on Signal Reconstruction from Edited Cepstrum

A feature extraction scheme for rolling element bearing (REB) fault diagnosis by editing the cepstrum of the original vibration is introduced in this paper. In the presented approach, the order analysis technology is utilized to convert an even-time-spaced scaled signal to an even-angle-spaced signal by resampling the acquired signal. The discrete lines belonging to gears are removed by editing the cepstrum. Then, the signal is reconstructed from the edited cepstrum. Lastly, clear characteristic frequencies related with the faulty REB can be obtained by the envelope spectrum analysis. Simulation studies indicate that this method can be employed to detect the REB faults in a gearbox with a high accuracy by removing the strong gear vibration disturbances.

Time-Frequency Analysis System of Rolling Element Bearing Based on Virtual Instrument

Rolling element bearing (REB) is one of important components in the condition monitoring and faults diagnosis of machinery. In this paper, a REB fault diagnosis system is presented, which is developed by using LabVIEW. In the system, vibration signals are picked by acceleration sensors and acquired by NI USB data acquisition card at first. Then, the fault diagnosis can be performed in the time-frequency domain by various time-frequency methods. The experiments show that the presented system can be used to extract the bearing fault features and diagnoses the failures effectively.

Modification of Autocorrelation Analysis Algorithm and its Application

Results of autocorrelation analysis algorithm by the LabVIEW are different from the theoretical results. To address the problem, a modification of autocorrelation analysis is proposed in this paper. In the proposed approach, the circular correlation and linear compensation are employed to solve the distortion problem in the original algorithm. Simulation results show that the method can reduce errors of autocorrelation analysis effectively.

Gear Fault Identification by Envelope Order Analysis Based on ADAMS Simulation

Due to the gear fault signal are often covered by complex noisy even under varying-speed running condition, and it is difficult to simulate the gear fault impact in practical work. To address the problem, a simulation scheme of gear fault is proposed in this paper. In the proposed approach, the gear fault model is built in UG at first. Subsequently, the model is imported in ADAMS, in which the meshing force instead of the vibration signal from a gearbox in real world is extracted, and then the envelope of the meshing force signal is extracted. Finally, the envelope order spectrum analysis scheme is used to obtain the feature order of gear faults. Simulation is conducted to verify the validity of the proposed method.