scholarly journals An Improved Dual-Kurtogram-Based T 2 Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zhiyuan Jiao ◽  
Wei Fan ◽  
Zhenying Xu
Keyword(s):  
Fault Diagnosis ◽  
Control Chart ◽  
Condition Monitoring ◽  
Control Limit ◽  
Normal Operation ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Envelope Analysis ◽  
Spectral Kurtosis ◽  
Vibration Data

Condition monitoring and compound fault diagnosis are crucial key points for ensuring the normal operation of rotating machinery. A novel method for condition monitoring and compound fault diagnosis based on the dual-kurtogram algorithm and multivariate statistical process control is established in this study. The core idea of this method is the capability of the dual-kurtogram in extracting subbands. Vibration data under normal conditions are decomposed by the dual-kurtogram into two subbands. Then, the spectral kurtosis (SK) of Subband I and the envelope spectral kurtosis (ESK) of Subband II are formulated to construct a control limit based on kernel density estimation. Similarly, vibration data that need to be monitored are constructed into two subbands by the dual-kurtogram. The SK of Subband I and the ESK of Subband II are calculated to derive T 2 statistics based on the covariance determinant. An alarm will be triggered when the T 2 statistics exceed the control limit and suitable subbands for square envelope analysis are adopted to obtain the characteristic frequency. Simulation and experimental data are used to verify the feasibility of the proposed method. Results confirm that the proposed method can effectively perform condition monitoring and fault diagnosis. Furthermore, comparison studies show that the proposed method outperforms the traditional T 2 control chart, envelope analysis, and empirical mode decomposition.

scholarly journals Oversizing Thread Diagnosis in Tapping Operation

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 537
Author(s):  
Alain Gil Del Val ◽  
Fernando Veiga ◽  
Mariluz Penalva ◽  
Miguel Arizmendi
Keyword(s):  
Control Chart ◽  
Manufacturing Systems ◽  
Spindle Motor ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Knowing That ◽  
On Line ◽  
High Level ◽  
Screw Threads ◽  
Multivariate Control

Automotive, railway and aerospace sectors require a high level of quality on the thread profiles in their manufacturing systems knowing that the tapping process is a complex manufacturing process and the last operation in a manufacturing cell. Therefore, a multivariate statistical process control chart, for each tap, is presented based on the principal components of the torque signal directly measured from spindle motor drive to diagnosis the thread profile quality. This on-line multivariate control chart has implemented an alarm to avoid defected screw threads (oversized). Therefore, it could work automatically without any operator intervention assessing the thread quality and the safety is guaranteed during the tapping process.

scholarly journals Fault Diagnosis of Industrial Gear Hobbing Machine

2020 ◽  
Vol 9 (3) ◽  
pp. 2039-2043
Keyword(s):  
Fault Diagnosis ◽  
Low Cost ◽  
Market Competition ◽  
Vibrational Analysis ◽  
High Quality ◽  
Mems Accelerometer ◽  
Spectral Kurtosis ◽  
Vibration Data ◽  
Gear Hobbing ◽  
Dynamic Time

The aim of this paper is to develop a fault diagnosis algorithm by vibrational analysis for an industrial gear hobbing machine. Gear Hobbing is the most dominant and profitable process for manufacturing high quality gears. In order to sustain the market competition gear manufacturers, need to produce high quality gears with minimum possible cost. However, catastrophic failures do occur in gear hobbing process which causes unexpected machine down time and revenue loss. These failures can be avoided by using condition monitoring approaches. In the proposed approach vibration data during different faults such as lubrication error, excessive feed rate, loose bearing error is collected from an industrial gear hobbing machine using three axis MEMS accelerometer. The collected data is analyzed and classified with spectral kurtosis and Dynamic Time Warping algorithm. The efficiency of the proposed approach is 90 percent as determined by experimental results. The proposed approach can provide a low-cost solution for predictive maintenance for gear hobbing industries..

scholarly journals Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
A. Romero ◽  
Y. Lage ◽  
S. Soua ◽  
B. Wang ◽  
T.-H. Gan
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Learning Algorithm ◽  
Health Assessment ◽  
Field Trials ◽  
Health Condition ◽  
Normal Operation ◽  
Wind Turbine Gearbox ◽  
Operation Conditions

Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry. This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on a machine learning algorithm that generates a baseline for the identification of deviations from the normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information. The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.

TQWT-assisted statistical process control method for condition monitoring and fault diagnosis of bearings in high-speed rail

2020 ◽  
pp. 1748006X2095832 ◽  
Author(s):  
Wei Fan ◽  
Hongtao Xue ◽  
Cai Yi ◽  
Zhenying Xu
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
High Speed ◽  
Control Method ◽  
Wavelet Coefficients ◽  
Frequency Method ◽  
Statistical Process ◽  
High Speed Rail ◽  
Distribution Method ◽  
Time Frequency

Condition monitoring and fault diagnosis of bearings in high-speed rail have attracted considerable attention in recent years, however, it’s still a hard work due to harsh environments with high speeds and high loads. A statistical condition monitoring and fault diagnosis method based on tunable Q-factor wavelet transform (TQWT) is developed in this study. The core idea of this method is that the TQWT can extract oscillatory behaviors of bearing faults. The vibration data under the normal condition are first decomposed by the TQWT into different wavelet coefficients. Two health indicators are then formulated by the dominant wavelet coefficients and the remaining coefficients for condition monitoring. The upper control limits are established using the one-sided confidence limit of the indicators by using the non-parametric bootstrap scheme. The Shewhart control charts on multiscale wavelet coefficients are constructed for fault diagnosis. We demonstrate the effectiveness of the proposed method by monitoring and diagnosing single and multiple railway axle bearing defects. Furthermore, the comparison studies show that the proposed method outperforms a traditional time-frequency method, the Wigner-Ville distribution method.

Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach

2018 ◽  
Vol 233 (3) ◽  
pp. 303-316 ◽  
Author(s):  
Miao He ◽  
David He ◽  
Jae Yoon ◽  
Thomas J Nostrand ◽  
Junda Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Dictionary Learning ◽  
Memory Storage ◽  
Vibration Monitoring ◽  
Planetary Gearbox ◽  
Storage And Retrieval ◽  
Vibration Data ◽  
Large Memory

Planetary gearboxes are widely used in the drivetrain of wind turbines. Planetary gearbox fault diagnosis is very important for reducing the downtime and maintenance cost and improving the safety, reliability, and life span of the wind turbines. The wind energy industry is currently using condition monitoring systems to collect massive real-time data and conventional vibratory analysis as a standard method for planetary gearbox condition monitoring. As an attractive option to process big data for fault diagnosis, deep learning can automatically learn features that otherwise require much skill, time, and experience. This article presents a new deep-learning-based method for wind turbine planetary gearbox fault diagnosis developed by a large memory storage and retrieval neural network with dictionary learning. The developed approach can automatically extract self-learned fault features from raw vibration monitoring data and perform planetary gearbox fault diagnosis without supervised fine-tuning process. From the raw vibration monitoring data, a dictionary is first learned by a large memory storage and retrieval with dictionary learning network. Based on the learned dictionary, a sparse representation of the raw vibration signals is generated by shift-invariant sparse coding and input to a large memory storage and retrieval network classifier to obtain fault diagnosis results. The structure of the large memory storage and retrieval with dictionary learning is determined by optimal selection of the sliding box size to generate sub-patterns from the vibration data. The effectiveness of the presented method is tested and validated with a set of seeded fault vibration data collected at a planetary gearbox test rig in laboratory. The validation results have shown a promising planetary gearbox fault diagnosis performance with the presented method.

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