Fault detection of gearboxes using synchro-squeezing transform

This paper presents a novel fault detection method for gearbox vibration signatures using the synchro-squeezing transform (SST). Premised upon the concept of time-frequency (TF) reassignment, the SST provides a sharp representation of signals in the TF plane compared to many popular TF methods. Additionally, it can also extract the individual components, called intrinsic mode functions or IMFs, of a nonstationary multi-component signal, akin to empirical mode decomposition. The rich mathematical structure based on the continuous wavelet transform makes synchro-squeezing a promising candidate for gearbox diagnosis, as such signals are frequently constituted out of multiple amplitude and frequency modulated signals embedded in noise. This work utilizes the decomposing power of the SST to extract the IMFs from gearbox signals, followed by the application of both condition indicators and fault detection to gearbox vibration data. For robust detection of faults in gear-motors, a fault detection technique based on time-varying auto-regressive coefficients of IMFs as features is utilized. The sequential Karhunen–Loeve transform is employed on the condition indicators to select the appropriate window sizes on which the SST can be applied. This approach promises improved fault detection capability compared to applying condition indicators directly to the raw data. Laboratory experimental data obtained from a drivetrain diagnostics simulator and seeded fault tests from a helicopter gearbox provide test beds to demonstrate the robustness of the proposed algorithm.

Download Full-text

Ensemble Noise-Reconstructed Empirical Mode Decomposition for Mechanical Fault Detection

Journal of Vibration and Acoustics ◽

10.1115/1.4023138 ◽

2013 ◽

Vol 135 (2) ◽

Cited By ~ 8

Author(s):

Jing Yuan ◽

Zhengjia He ◽

Jun Ni ◽

Adam John Brzezinski ◽

Yanyang Zi

Keyword(s):

Fault Detection ◽

Empirical Mode Decomposition ◽

Measured Signal ◽

Accelerometer Data ◽

Intrinsic Mode Functions ◽

Time Frequency ◽

Mode Decomposition ◽

Mixing Problem ◽

Emd Method ◽

Mode Mixing

Various faults inevitably occur in mechanical systems and may result in unexpected failures. Hence, fault detection is critical to reduce unscheduled downtime and costly breakdowns. Empirical mode decomposition (EMD) is an adaptive time-frequency domain signal processing method, potentially suitable for nonstationary and/or nonlinear processes. However, the EMD method suffers from several problems such as mode mixing, defined as intrinsic mode functions (IMFs) with incorrect scales. In this paper, an ensemble noise-reconstructed EMD method is proposed to ameliorate the mode mixing problem and denoise IMFs for enhancing fault signatures. The proposed method defines the IMF components as an ensemble mean of EMD trials, where each trial is obtained by sifting signals that have been reconstructed using the estimated noise present in the measured signal. Unlike traditional denoising methods, the noise inherent in the input data is reconstructed and used to reduce the background noise. Furthermore, the reconstructed noise helps to project different scales of the signal onto their corresponding IMFs, instrumental in alleviating the mode mixing problem. Two critical issues concerned in the method, i.e., the noise estimation strategy and the number of EMD trials required for denoising are discussed. Furthermore, a comprehensive noise-assisted EMD method is proposed, which includes the proposed method and ensemble EMD (EEMD). Numerical simulations and experimental case studies on accelerometer data collected from an industrial shaving process are used to demonstrate and validate the proposed method. Results show that the proposed method can both detect impending faults and isolate multiple faults. Hence, the proposed method can act as a promising tool for mechanical fault detection.

Download Full-text

Gear Fault Diagnosis Using Synchro-Squeezing Transform Based Feature Analysis

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.569-570.449 ◽

2013 ◽

Vol 569-570 ◽

pp. 449-456 ◽

Cited By ~ 2

Author(s):

Budhaditya Hazra ◽

Sriram Narasimhan

Keyword(s):

Signal Processing ◽

Fault Diagnosis ◽

Mathematical Structure ◽

Time Frequency ◽

Mode Decomposition ◽

Gear Fault ◽

Gear Fault Diagnosis ◽

The Rich ◽

The Individual ◽

Non Stationary Signal

Synchro-squeezing transform has recently emerged as a powerful signal processing tool in non-stationary signal processing. Premised upon the concept of time-frequency (TF) reassignment, its basic objective is to provide a sharper representation of signals in the TF plane and extract the individual components of a non-stationary multi-component signal, akin to empirical mode decomposition (EMD). The rich mathematical structure based on continuous wavelet transform (CWT) makes synchro-squeezing powerful for gear fault diagnosis, as faulty gear signal is frequently constituted out of multiple amplitude-modulated and frequency-modulated signals embedded in noise. This work utilizes the decomposing power of synchro-squeezing transform to extract the IMFs from a gear signal followed by the application of standard gearbox condition indicators which promises greater prognostic power than that can be achieved by applying condition indictors directly to the inherently complex gear signals. The efficacy and the robustness of the algorithm are demonstrated with the aid of practical experimental data obtained from a helicopter gear box.

Download Full-text

Order Tracking Using Variational Mode Decomposition to Detect Gear Faults under Speed Fluctuations

Annual Conference of the PHM Society ◽

10.36001/phmconf.2019.v11i1.787 ◽

2019 ◽

Vol 11 (1) ◽

Author(s):

Madhurjya Dev Choudhury ◽

Liu Hong ◽

Jaspreet Singh Dhupia

Keyword(s):

Fault Detection ◽

Reference Signal ◽

Vibration Signal ◽

Operating Speed ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Order Tracking ◽

Mode Decomposition ◽

Gearbox Vibration

Fault detection in gearboxes plays a significant role in ensuring their reliability. Vibration signals collected during gearbox operation contain a wealth of valuable condition information that can be exploited for fault detection. However, in an industrial environment machine operating speed always fluctuates around its nominal value, which causes smearing of the gearbox vibration spectrum. Considering operating speed fluctuation and multi-component nature of measured gearbox vibration signals, an order-tracking method combining the variational mode decomposition (VMD) and the fast dynamic time warping (FDTW) is proposed in this paper. Firstly, the multi-component vibration signal is decomposed into several intrinsic mode functions (IMFs) using VMD in order to extract a signal component with higher signal-to-noise ratio (SNR). Then, the sensitive fault information carrying IMF is exploited to estimate the instantaneous speed profile in order to construct the shaft rotational vibration signal. The measured vibration signal is then resampled based on the optimal warping path obtained by FDTW, which performs an “elastic” stretching and compression along the time axis of the extracted shaft vibration signal with respect to a sinusoidal reference signal of constant shaft rotational frequency. Finally, the gear fault is detected by constructing the order spectrum of the resampled vibration signal. The effectiveness of the proposed algorithm is demonstrated using simulation results.

Download Full-text

Fault severity detection of a worm gearbox based on several feature extraction methods through a developed condition monitoring system

SN Applied Sciences ◽

10.1007/s42452-020-04131-w ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Berkan Hızarcı ◽

Rafet Can Ümütlü ◽

Zeki Kıral ◽

Hasan Öztürk

Keyword(s):

Fault Detection ◽

Condition Monitoring ◽

Extraction Methods ◽

Poincaré Plot ◽

Vibration Data ◽

Sliding Motion ◽

Fault Severity ◽

Fault Features ◽

Gearbox Vibration ◽

Condition Monitoring System

AbstractThis study presents the severity detection of pitting faults on worm gearbox through the assessment of fault features extracted from the gearbox vibration data. Fault severity assessment on worm gearbox is conducted by the developed condition monitoring instrument with observing not only traditional but also multidisciplinary features. It is well known that the sliding motion between the worm gear and wheel gear causes difficulties about fault detection on worm gearboxes. Therefore, continuous monitoring and observation of different types of fault features are very important, especially for worm gearboxes. Therefore, in this study, time-domain statistics, the features of evaluated vibration analysis method and Poincaré plot are examined for fault severity detection on worm gearbox. The most reliable features for fault detection on worm gearbox are determined via the parallel coordinate plot. The abnormality detection during worm gearbox operation with the developed system is performed successfully by means of a decision tree.

Download Full-text

Spatiotemporal Trend and Variability of Precipitation in Taiwan Based on Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD)

10.5194/egusphere-egu21-10609 ◽

2021 ◽

Author(s):

Chun-Hsiang Tang ◽

Christina W. Tsai

Keyword(s):

Time Series ◽

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Short Term ◽

Time Frequency ◽

Spatial Locality ◽

Temperature And Precipitation ◽

Mode Decomposition ◽

Climatic Scenarios

AbstractMost of the time series in nature are nonlinear and nonstationary affected by climate change particularly. It is inevitable that Taiwan has also experienced frequent drought events in recent years. However, drought events are natural disasters with no clear warnings and their influences are cumulative. The difficulty of detecting and analyzing the drought phenomenon remains. To deal with the above-mentioned problem, Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) is introduced to analyze the temperature and rainfall data from 1975~2018 in this study, which is a powerful method developed for the time-frequency analysis of nonlinear, nonstationary time series. This method can not only analyze the spatial locality and temporal locality of signals but also decompose the multiple-dimensional time series into several Intrinsic Mode Functions (IMFs). By the set of IMFs, the meaningful instantaneous frequency and the trend of the signals can be observed. Considering stochastic and deterministic influences, to enhance the accuracy this study also reconstruct IMFs into two components, stochastic and deterministic, by the coefficient of auto-correlation.In this study, the influences of temperature and precipitation on the drought events will be discussed. Furthermore, to decrease the significant impact of drought events, this study also attempts to forecast the occurrences of drought events in the short-term via the Artificial Neural Network technique. And, based on the CMIP5 model, this study also investigates the trend and variability of drought events and warming in different climatic scenarios.&#160;Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought

Download Full-text

Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD

Applied Sciences ◽

10.3390/app10186376 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6376 ◽

Cited By ~ 1

Author(s):

Yihan Wang ◽

Zhonghui Fan ◽

Hongmei Liu ◽

Xin Gao

Keyword(s):

Fault Diagnosis ◽

Information Entropy ◽

Entropy Method ◽

Characteristic Matrix ◽

Intrinsic Mode Functions ◽

Noise Component ◽

Time Frequency ◽

Frequency Information ◽

Operation Conditions ◽

Mode Decomposition

Planetary gearboxes are more and more widely used in large and complex construction machinery such as those used in aviation, aerospace fields, and so on. However, the movement of the gear is a typical complex motion and is often under variable conditions in real environments, which may make vibration signals of planetary gearboxes nonlinear and nonstationary. It is more difficult and complex to achieve fault diagnosis than to fix the axis gearboxes effectively. A fault diagnosis method for planetary gearboxes based on improved complementary ensemble empirical mode decomposition (ICEEMD)-time-frequency information entropy and variable predictive model-based class discriminate (VPMCD) is proposed in this paper. First, the vibration signal of planetary gearboxes is decomposed into several intrinsic mode functions (IMFs) by using the ICEEMD algorithm, which is used to determine the noise component by using the magnitude of the entropy and to remove the noise components. Then, the time-frequency information entropy of intrinsic modal function under the new decomposition is calculated and regarded as the characteristic matrix. Finally, the fault mode is classified by the VPMCD method. The experimental results demonstrate that the method proposed in this paper can not only solve the fault diagnosis of planetary gearboxes under different operation conditions, but can also be used for fault diagnosis under variable operation conditions. Simultaneously, the proposed method is superior to the wavelet entropy method and variational mode decomposition (VMD)-time-frequency information entropy.

Download Full-text

The Application of Improved HHT to Harmonic Analysis and Fault Detection of Power System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.354-355.1406 ◽

2011 ◽

Vol 354-355 ◽

pp. 1406-1411

Author(s):

Wen Hua Han ◽

Hai Xia Ren ◽

Xu Chen ◽

Xiao Juan Tao

Keyword(s):

Fault Detection ◽

Power System ◽

Harmonic Analysis ◽

Harmonic Signal ◽

Ensemble Empirical Mode Decomposition ◽

Frequency Domain Analysis ◽

Analysis Method ◽

Hilbert Huang Transform ◽

Time Frequency ◽

Mode Decomposition

Hilbert-Huang transform (HHT) is a new time-frequency-domain analysis method, which is suitable for non-stationary and nonlinear signals. In this paper, endpoint continuation and ensemble empirical mode decomposition (EEMD) decomposition method are introduced to improve the HHT, which solve the endpoint winger and modal aliasing problem. The improved HHT (IHHT) is used for analyzing the harmonic signal and detecting the fault signal of power system. Simulation results show that IHHT is feasible and effective for harmonic analysis and fault detection.

Download Full-text

Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽

10.1190/geo2015-0489.1 ◽

2016 ◽

Vol 81 (5) ◽

pp. V365-V378 ◽

Cited By ~ 74

Author(s):

Wei Liu ◽

Siyuan Cao ◽

Yangkang Chen

Keyword(s):

Empirical Mode Decomposition ◽

Frequency Analysis ◽

Synthetic Data ◽

Ensemble Empirical Mode Decomposition ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Decomposition Approach ◽

Time Frequency Analysis ◽

Time Frequency ◽

Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

Download Full-text

Time-Frequency Characteristics Analysis on Vibration Signals of a Three-Supported Rotor System with Misalignment

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.2-3.717 ◽

2011 ◽

Vol 2-3 ◽

pp. 717-721 ◽

Cited By ~ 1

Author(s):

Xiao Xuan Qi ◽

Mei Ling Wang ◽

Li Jing Lin ◽

Jian Wei Ji ◽

Qing Kai Han

Keyword(s):

Wavelet Packet ◽

Vibration Signal ◽

Dominant Frequency ◽

Intrinsic Mode Functions ◽

Time Frequency ◽

Mode Decomposition ◽

Reconstructed Signal ◽

Characteristics Analysis ◽

Novel Method ◽

Misalignment Fault

In light of the complex and non-stationary characteristics of misalignment vibration signal, this paper proposed a novel method to analyze in time-frequency domain under different working conditions. Firstly, decompose raw misalignment signal into different frequency bands by wavelet packet (WP) and reconstruct it in accordance with the band energy to remove noises. Secondly, employ empirical mode decomposition (EMD) to the reconstructed signal to obtain a certain number of stationary intrinsic mode functions (IMF). Finally, apply further spectrum analysis on the interested IMFs. In this way, weak signal is caught and dominant frequency is picked up for the diagnosis of misalignment fault. Experimental results show that the proposed method is able to detect misalignment fault characteristic frequency effectively.

Download Full-text