scholarly journals Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

2018 ◽  
Vol 211 ◽  
pp. 21005 ◽  
Author(s):  
Kenisuomo C. Luwei ◽  
Jyoti K. Sinha ◽  
Akilu Yunusa-Kaltungo ◽  
Keri Elbhbah
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Critical Speed ◽  
Current Paper ◽  
Rotating Machines ◽  
Test Rig ◽  
Vibration Data ◽  
Identical Machines ◽  
Research Studies

Earlier research studies have suggested the unified vibration-based approach for fault diagnosis (FD) in identical machines with different foundation flexibilities and multi-rotating speeds. Intially the acceleration-based features were used for this approach then further work optimised the approach by combining acceleration and velocity features from vibration data for analysis. However the optimised approach was only tested on the identical machines rotating at different speeds below the machine’s first critical speed. The current paper tends to observe the optimised approach when applied to a test rig operating below and above the machine’s first critical speed.

scholarly journals Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

Machines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 59 ◽  
Author(s):  
Kenisuomo C. Luwei ◽  
Akilu Yunusa-Kaltungo ◽  
Yusuf A. Sha’aban
Keyword(s):  
Data Fusion ◽  
Condition Monitoring ◽  
Real Life ◽  
General Concept ◽  
Rotating Machines ◽  
Classification Approach ◽  
Vibration Data ◽  
Artificial Neural ◽  
Machine Health ◽  
Artificial Neural Network Ann

The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines.

Optimum Design of Journal Bearings Dimensions for Rotating Machines

2020 ◽  
Vol 38 (10A) ◽  
pp. 1481-1488
Author(s):  
Tariq M. Hammza ◽  
Ehab N. Abas ◽  
Nassear R. Hmoad
Keyword(s):  
Aspect Ratio ◽  
Dynamic Response ◽  
Numerical Solution ◽  
Critical Speed ◽  
Design Method ◽  
Considerable Effect ◽  
Journal Bearings ◽  
Rotating Machines ◽  
Bearing Clearance ◽  
Study Results

The values of Many parameters which involve in the design of fluid film journal bearings mainly depend on the bearing applied load when using the conventional design method to design the journal bearings, in this study, as well as applied bearing load, the dynamic response and critical speed have been used to calculate the dimensions of journal bearings. In the field of rotating machine, especially a heavy-duty rotating machines, the critical speed and response are the main parameters that specify bearing dimensions. The bearing aspect ratio (bearing length to bore diameter) and bearing clearance have been determined based on rotor maximum critical speed and minimum response displacement. The analytical solution of rotor Eq. of motion was verified by numerical solution via using ANSYS Mechanical APDL 18.0 and by comparing the numerical solution with the preceding study. The final study results clearly showed that the bearing aspect ratio has little effect on the critical speed, but it has a high effect on the dynamic response also the bearing clearance has little effect on the critical speed and considerable effect on the dynamic response. The study showed that the more accurate values of bearing aspect ratio to make the response of rotor as low as possible are about 0.65 - 1 and bearing percent clearance is about 0.15 - 0.2 for different rotor dimensions.

scholarly journals Reliability of Measured Data for pH Sensor Arrays with Fault Diagnosis and Data Fusion Based on LabVIEW

Sensors ◽  
2013 ◽  
Vol 13 (12) ◽  
pp. 17281-17291 ◽  
Author(s):  
Yi-Hung Liao ◽  
Jung-Chuan Chou ◽  
Chin-Yi Lin
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Ph Sensor ◽  
Sensor Arrays ◽  
Measured Data

Fault Diagnosis of New Certain Mine Sweeping Plough’s Electrical Control System Based on Data Fusion

2015 ◽  
Vol 713-715 ◽  
pp. 539-543
Author(s):  
Yong Zhao ◽  
Xiao Qiang Yang ◽  
Yin Hua Xu ◽  
Jian Bin Li
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Complex Structure ◽  
Evidence Theory ◽  
System Level ◽  
Sensor Failure ◽  
Advanced Technique ◽  
Electrical Control

The fault diagnosis of electrical control system of certain type mine sweeping vehicle is difficult due to its complex structure and advanced technique. So in the multi-sensor failure diagnosis process, as a result of various reasons, such as the existence of measurement noise, diagnosis knowledge incomplete and so on, it makes the fault diagnosis uncertainty and affects the reliability and the accuracy of the diagnosis result. This article according to the analysis of electrical control system's fault characteristic of the mine sweeping plough’s, proposes a technique based on data fusion fault diagnosis method. The diagnosis process is divided into the sub system and the system-level, the subsystem uses the BP neural network to classify the fault mode, the system-level uses the D-S evidence theory carries on the comprehensive decision judgment for the whole system's fault. Application shows if some sub-neural network diagnosis has error, using D-S evidence theory fusion can effectively improve the accuracy of diagnosis.

scholarly journals Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 36
Author(s):  
Xiaoan Yan ◽  
Yadong Xu ◽  
Daoming She ◽  
Wan Zhang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Identification Accuracy ◽  
Learning Ability ◽  
Rolling Bearings ◽  
Softmax Classifier ◽  
Vibration Data ◽  
Fault Recognition ◽  
Fault Features ◽  
Latent Features

Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common and key problem in existing deep learning models. Additionally, its anti-noise capability may face a decline when VAE is used to analyze bearing vibration data under loud environmental noise. Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE) for the reliable fault diagnosis of bearings. Within the proposed method, a robust network, named variational denoising auto-encoder (VDAE), is, first, designed by integrating VAE and a denoising auto-encoder (DAE). Subsequently, a stacked variational denoising auto-encoder (SVDAE) architecture is constructed to extract the robust and discriminative latent fault features via stacking VDAE networks layer on layer, wherein the important parameters of the SVDAE model are automatically determined by employing a novel meta-heuristic intelligent optimizer known as the seagull optimization algorithm (SOA). Finally, the extracted latent features are imported into a softmax classifier to obtain the results of fault recognition in rolling bearings. Experiments are conducted to validate the effectiveness of the proposed method. The results of analysis indicate that the proposed method not only can achieve a high identification accuracy for different bearing health conditions, but also outperforms some representative deep learning methods.

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..

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