A new real-time signal processing approach for frequency-varying machinery

2017 ◽  
Vol 24 (11) ◽  
pp. 2359-2368 ◽  
Author(s):  
Jie Zhang ◽  
Hongli Gao ◽  
Qiyue Liu ◽  
Christopher Grebe
Keyword(s):  
Condition Monitoring ◽  
Vibration Signal ◽  
Time Signal ◽  
Detailed Knowledge ◽  
Mixing Processes ◽  
Time Frequency ◽  
Analysis Methods ◽  
Power Spectral ◽  
New Processing ◽  
The Stability

Development of condition monitoring approaches has played a key role in the stability and safety of frequency-varying machinery operations. Conventional time–frequency analysis methods suffer problems such as analysis results being too complex to realize highly intelligent and automated condition monitoring systems. Blind source separation is an attractive tool due to its excellent performance in separating defect source signals from their mixtures without detailed knowledge of sources and mixing processes; however, it can only be applied under some strict conditions. In this paper, a nonuniform sampling model is built and a new processing algorithm of frequency-varying signal is proposed. The relationship between the power spectral density (PSD) of the vibration signal of frequency-varying machinery and frequencies at different rotational speeds is derived. The proposed method can adaptively eliminate the influence of the varying rotational speed in the revised PSD. Some classical signal analysis methods are implemented to compare with the proposed approach by simulations. An experiment has been conducted by using a JD-1 wheel/rail simulation facility to illustrate the effectiveness of the proposed method.

scholarly journals Vibration Condition Monitoring: Latest Trend and Review

2015 ◽  
Vol 773-774 ◽  
pp. 139-143
Author(s):  
K.H. Hui ◽  
L.M. Hee ◽  
M. Salman Leong ◽  
Ahmed M. Abdelrhman
Keyword(s):  
Decision Making ◽  
Fourier Transform ◽  
Condition Monitoring ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Machine Learning Techniques ◽  
Vibration Condition ◽  
The Past ◽  
Analysis Methods ◽  
Vibration Signal Analysis

Vibration analysis has proven to be the most effective method for machine condition monitoring to date. Various effective signal analysis methods to analyze and extract fault signature that embedded in the raw vibration signals have been introduced in the past few decades such as fast Fourier transform (FFT), short time Fourier transform (STFT), wavelets analysis, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), etc. however, these is still a need for human to interpret vibration signature of faults and it is regarded as one of the major challenge in vibration condition monitoring. Thus, most recent researches in vibration condition monitoring revolved around using Artificial Intelligence (AI) techniques to automate machinery faults detection and diagnosis. The most recent literatures in this area show that researches are mainly focus on using machine learning techniques for data fusion, features fusion, and also decisions fusion in order to achieve a higher accuracy of decision making in vibration condition monitoring. This paper provides a review on the most recent development in vibration signal analysis methods as well as the AI techniques used for automated decision making in vibration condition monitoring in the past two years.

An Improved Random Decrement Algorithm and Applications

2011 ◽  
Vol 121-126 ◽  
pp. 4372-4376
Author(s):  
Qing Wei Ye ◽  
Zhi Min Feng ◽  
Hai Gang Hu
Keyword(s):  
Response Function ◽  
Vibration Signal ◽  
Recognition Algorithm ◽  
Mode Analysis ◽  
Time Signal ◽  
Signal Acquisition ◽  
Free Response ◽  
Time Frequency ◽  
Random Decrement Technique ◽  
Random Decrement

The free response function is the foundation of mode analysis and recognition of vibration signal, and random decrement algorithm is the commonly used classical algorithm of extracting the free response function. But under the restriction of engineering conditions, it may be impossible for long-time signal acquisition, which makes the number of sample points fail to meet the requirements of the random decrement algorithm, causing the extracted free response signals to contain strong noise and other influencing factors. Aiming at the shortcomings of the existing random decrement technique, this paper proposes an improved random decrement algorithm based on multi-secant method, which can get satisfactory free response signals with short vibration response signals to provide excellent basis of analysis for the vibration mode recognition algorithm of various time-frequency domains. Actual engineering tests confirm that the improved algorithm greatly improves the precision of extracting free response signals while basically keeping the computation speed unchanged, it has high application value.

scholarly journals A Study on Defect Identification of Planetary Gearbox under Large Speed Oscillation

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Xingxing Jiang ◽  
Shunming Li ◽  
Qian Wang
Keyword(s):  
Condition Monitoring ◽  
Vibration Signal ◽  
Estimation Methods ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Ridge Estimation ◽  
Practical Engineering ◽  
Conventional Methods ◽  
Tracking Time ◽  
Large Speed

Rotational speed of a reference shaft is the key information for planetary gearbox condition monitoring under nonstationary conditions. As the time-variant speed and load of planetary gearboxes result in time-variant characteristic frequencies as well as vibration magnitudes, the conventional methods tracking time-frequency ridge perform a poor robustness, especially for large speed variations. In this paper, two schemes, time-frequency ridge fusion and logarithm transformation, are proposed to track the targeted ridge curve reliably. Meanwhile, the identified ridge curve by logarithm scheme can be further refined by the time-frequency ridge fusion scheme. Hence, a procedure involving the proposed ridge estimation methods is presented to diagnose the planetary gearbox defects. Two simulation signals and a vibration signal collected from a planetary gearbox in practical engineering (provided by the conference on condition monitoring of machinery in nonstationary operations (CMMNO)) are used to verify the proposed methods. It is validated that the proposed methods can well-track the targeted ridge curve compared with two conventional methods. As a result, the characteristic frequency of each component in the planetary gearbox is clearly demonstrated and the inner race defect of one of the planet bearings is successfully discovered in the order spectrum depending on the derived expression of planet bearing fault frequency.

scholarly journals Bearing Condition Monitoring based on the Indicator Generated in Time-frequency Domain

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Teng Wang ◽  
Zheng Liu ◽  
Guoliang Lu
Keyword(s):  
Condition Monitoring ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Data Driven ◽  
Time Frequency ◽  
Vibration Data ◽  
Bearing Condition Monitoring ◽  
Condition Monitoring Systems ◽  
Early Fault Detection ◽  
Condition Indicator

Most condition monitoring systems rely on system-driven generation of indicators or features for early fault detection. However, this strategy requires the prior knowledge on the system kinematics and/or exact structure parameters of monitored system. To address this problem, this paper presents a novel condition monitoring framework where the condition indicator is generated via data-driven method. In this framework, the time-frequency periodogram is extracted from raw vibration signal first. Then, the acquired time-frequency periodogram is mapped by pseudo Perron vector, which is learned from vibration data, to generate the condition indicator. Finally, the bearing can be monitored via analyzing this indicator using gaussian based control chart. Based on experimental results on a publicly-available database, we show the effectiveness of presented framework for early fault detectionin the continuous operation of rolling bearing, indicating its great potentials in real engineering applications.

The Method of Random Vibration Signal Transformation from Frequency to Time Domain Based on FIR Digit Filter

2012 ◽  
Vol 479-481 ◽  
pp. 1277-1282
Author(s):  
Jie Li ◽  
Jin Lu Pang ◽  
Xiao Yan Wang ◽  
Hai Lan ◽  
Zhi Yong Wang
Keyword(s):  
Time Domain ◽  
Random Vibration ◽  
Vibration Signal ◽  
Ar Model ◽  
Time Signal ◽  
Time Domain Signal ◽  
Power Spectral ◽  
Parameters Analysis ◽  
Vibration Time ◽  
The Given

Based on finite unit impulse response, FIR digit filter is designed according as the given power spectral density (PSD) characteristics beforehand. By controlling the parameters of filter, amplitude-phase characteristics that can meet completely the preconditions and demand is obtained. And then white noise is inputted to filter, random vibration time signal is generated. Moreover, the method of parameters analysis of AR autoregressive model is used to estimate and validate power spectrum of the gotten random vibration time domain signal. The result shows that the accuracy and error of PSD between AR model and the given function are within the range of the permitted value. And random vibration time signal obtained by using the redesigned FIR filter can meet absolutely the precision demand beforehand in the simulation and test.

scholarly journals A novel time–frequency–space method with parallel factor theory for big data analysis in condition monitoring of complex system

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091694 ◽  
Author(s):  
Liu Yang ◽  
Hanxin Chen ◽  
Yao Ke ◽  
Lang Huang ◽  
Qi Wang ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Condition Monitoring ◽  
Spatial Information ◽  
Component Analysis ◽  
Parallel Factor Analysis ◽  
Time Signal ◽  
Dynamic Feature ◽  
Frequency Space ◽  
Time Frequency ◽  
Parallel Factor

The spatial information of the signal is neglected by the conventional frequency/time decompositions such as the fast Fourier transformation, principal component analysis, and independent component analysis. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of parallel factor analysis, which is known as a unique multi-way decomposition. The parallel factor analysis was used to decompose the wavelet transformed ongoing diagnostic channel–frequency–time signal and each atom is trilinearly decomposed into spatial, spectral, and temporal signature. The time–frequency–space characteristics of the single-source fault signal was extracted from the multi-source dynamic feature recognition of mechanical nonlinear multi-failure mode and the corresponding relationship between the nonlinear variable, multi-fault mode, and multi-source fault features in time, frequency, and space was obtained. In this article, a new method for the multi-fault condition monitoring of slurry pump based on parallel factor analysis and continuous wavelet transform was developed to meet the requirements of automatic monitoring and fault diagnosis of industrial process production lines. The multi-scale parallel factorization theory was studied and a three-dimensional time–frequency–space model reconstruction algorithm for multi-source feature factors that improves the accuracy of mechanical fault detection and intelligent levels was proposed.

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

2012 ◽  
Vol 430-432 ◽  
pp. 1939-1942 ◽  
Author(s):  
Chuan Hui Wu ◽  
Yan Gao ◽  
Yu Guo
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Condition Monitoring ◽  
Vibration Signal ◽  
Signal Acquisition ◽  
Diagnosis System ◽  
Time Frequency ◽  
Machinery Condition Monitoring ◽  
Temperature Signal ◽  
Fault Diagnosis System

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.

Turning Dynamics: Part 1 — Experimental Analysis

2012 ◽  
Author(s):  
Eric B. Halfmann ◽  
C. Steve Suh ◽  
N. P. Hung
Keyword(s):  
Instantaneous Frequency ◽  
Period Doubling ◽  
Displacement Sensor ◽  
Laser Displacement Sensor ◽  
Turning Process ◽  
Time Frequency ◽  
Spectral Components ◽  
Tool Vibrations ◽  
Exact Moment ◽  
The Stability

The workpiece and tool vibrations in a lathe are experimentally studied to establish improved understanding of cutting dynamics that would support efforts in exceeding the current limits of the turning process. A Keyence laser displacement sensor is employed to monitor the workpiece and tool vibrations during chatter-free and chatter cutting. A procedure is developed that utilizes instantaneous frequency (IF) to identify the modes related to measurement noise and those innate of the cutting process. Instantaneous frequency is shown to thoroughly characterize the underlying turning dynamics and identify the exact moment in time when chatter fully developed. That IF provides the needed resolution for identifying the onset of chatter suggests that the stability of the process should be monitored in the time-frequency domain to effectively detect and characterize machining instability. It is determined that for the cutting tests performed chatters of the workpiece and tool are associated with the changing of the spectral components and more specifically period-doubling bifurcation. The analysis presented provides a view of the underlying dynamics of the lathe process which has not been experimentally observed before.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals Noise power spectral density scaled SNR response estimation with restricted range search for sound source localisation using unmanned aerial vehicles

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Benjamin Yen ◽  
Yusuke Hioka
Keyword(s):  
Spectral Density ◽  
Noise Reduction ◽  
Power Spectral Density ◽  
Sound Source ◽  
Processing Algorithm ◽  
Post Processing ◽  
Time Frequency ◽  
Distance Error ◽  
Rotor Noise ◽  
Power Spectral

Abstract A method to locate sound sources using an audio recording system mounted on an unmanned aerial vehicle (UAV) is proposed. The method introduces extension algorithms to apply on top of a baseline approach, which performs localisation by estimating the peak signal-to-noise ratio (SNR) response in the time-frequency and angular spectra with the time difference of arrival information. The proposed extensions include a noise reduction and a post-processing algorithm to address the challenges in a UAV setting. The noise reduction algorithm reduces influences of UAV rotor noise on localisation performance, by scaling the SNR response using power spectral density of the UAV rotor noise, estimated using a denoising autoencoder. For the source tracking problem, an angular spectral range restricted peak search and link post-processing algorithm is also proposed to filter out incorrect location estimates along the localisation path. Experimental results show the proposed extensions yielded improvements in locating the target sound source correctly, with a 0.0064–0.175 decrease in mean haversine distance error across various UAV operating scenarios. The proposed method also shows a reduction in unexpected location estimations, with a 0.0037–0.185 decrease in the 0.75 quartile haversine distance error.

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