scholarly journals Use of the Correlation Coefficient for Rotating Machine Monitoring

2021 ◽  
pp. 30-37
Author(s):  
Aimé Joseph Oyobé Okassa ◽  
Colince Welba ◽  
Jean Pierre Ngantcha ◽  
Pierre Ele
Keyword(s):  
Correlation Coefficient ◽  
Ball Bearing ◽  
Production Systems ◽  
Rotating Machines ◽  
Decision Tools ◽  
Vibration Data ◽  
Rotating Machine ◽  
Reference Window ◽  
Approximation Coefficients ◽  
Types Of Information

The use of electronics and computer technology in production systems has greatly improved the quality of our industrial products. The productivity of these installations is a function of the maintenance quality applied to the equipment. Several methods are used to monitor the functioning of industrial installations. One of these methods is vibration analysis. The vibration signals from the rotating machines support several types of information related to the working state of the production tool. The processing of this information makes it possible to have decision tools for maintenance. In this work, we propose a method of anticipating the maintenance of rotating machines. The algorithm we propose starts with the removal of 512 point windows during the running time of the ball bearing. Each signal is decomposed by DWT: we obtain the approximation coefficients. These coefficients make it possible to determine the correlation coefficient between the so-called reference window and the other windows following the functioning of the ball bearing. The correlation coefficient is then the fundamental element of the decision. This algorithm has been applied to real vibration data and the results are encouraging.

scholarly journals Identification of Unbalanced Forces and Foundation Parameters of Rotating Machines from Vibration Measurements

2008 ◽  
Vol 31 (2) ◽  
pp. 84-94
Author(s):  
N.G. Nalitoela
Keyword(s):  
Rotating Machines ◽  
Identification Procedure ◽  
Vibration Measurements ◽  
Unbalanced Mass ◽  
Vibration Data ◽  
Rotating Machine ◽  
Dynamic Forces ◽  
Stiffness And Damping ◽  
Foundation Parameters ◽  
Machine Structure

A technique is described to use measured vibration data of a rotating machine and its foundation to identify unbalanced forces, stiffness and damping parameters of the mountings, and the parameters of the foundation. It is based on an idealisation treating the rotor, the machine structure and the foundation as rigid masses supported by springs and dampers. Operational vibration data of the machine and its foundation before andafter the rotating unbalanced forces are perturbed by adding unbalanced mass to the rotor are used in the identification procedure. Once the parameters are identified, dynamic forces transmitted to the foundation can be estimated. The technique is demonstrated using simulated example for a machine with a two bearings rotor.

scholarly journals Effective vibration-based condition monitoring (eVCM) of rotating machines

2017 ◽  
Vol 23 (3) ◽  
pp. 279-296 ◽  
Author(s):  
Akilu Yunusa-kaltungo ◽  
Jyoti K. Sinha
Keyword(s):  
Frequency Domain ◽  
Condition Monitoring ◽  
Lessons Learned ◽  
Practical Implementation ◽  
Rotating Machines ◽  
Data Combination ◽  
Content Type ◽  
Vibration Data ◽  
Rotating Machine ◽  
Faults Diagnosis

Purpose The purpose of this paper is mainly to highlight how a simplified and streamlined approach to the condition monitoring (CM) of industrial rotating machines through the application of frequency domain data combination can effectively enhance the eMaintenance framework. Design/methodology/approach The paper commences by providing an overview to the relevance of maintenance excellence within manufacturing industries, with particular emphasis on the roles that rotating machines CM of rotating machines plays. It then proceeds to provide details of the eMaintenance as well as its possible alignment with the introduced concept of effective vibration-based condition monitoring (eVCM) of rotating machines. The subsequent sections of the paper respectively deal with explanations of data combination approaches, experimental setups used to generate vibration data and the theory of eVCM. Findings This paper investigates how a simplified vibration-based rotating machinery faults classification method based on frequency domain data combination can increase the feasibility and practicality of eMaintenance. Research limitations/implications The eVCM approach is based on classifying data acquired under several experimentally simulated conditions on two different machines using combined higher order signal processing parameters so as to reduce CM data requirements. Although the current study was solely based on the application of vibration data acquired from rotating machines, the knowledge exchange platform that currently dominates present day scientific research makes it very likely that the lessons learned from the development of eVCM concept can be easily transferred to other scientific domains that involve continuous CM such as medicine. Practical implications The concept of eMaintenance as a cost-effective and smart means of increasing the autonomy of maintenance activities within industries is rapidly growing in maintenance-related literatures. As viable as the concept appears, the achievement of its optimum objectives and full deployment to the industry is still subjective due to the complexity and data intensiveness of conventional CM practices. In this paper, an eVCM approach is proposed so that rotating machine faults can be effectively detected and classified without the need for repetitive analysis of measured data. Social implications The main strength of eVCM lies in the fact that it permits the sharing of historical vibration data between identical rotating machines irrespective of their foundation structures and speed differences. Since eMaintenance is concerned with driving maintenance excellence, eVCM can potentially contribute towards its optimisation as it cost-effectively streamlines faults diagnosis. This therefore implies that the simplification of vibration-based CM of rotating machines positively impacts the society with regard to the possibility of reducing how much time is actually spent on the accurate detection and classification of faults. Originality/value Although the currently existing body of literature already contains studies that have attempted to show how the combination of measured vibration data from several industrial machines can be used to establish a universal vibration-based faults diagnosis benchmark for incorporation into eMaintenance framework, these studies are limited in the scope of faults, severity and rotational speeds considered. In the current study, the concept of multi-faults, multi-sensor, multi-speed and multi-rotating machine data combination approach using frequency domain data fusion and principal components analysis is presented so that faults diagnosis features for identical rotating machines with different foundations can be shared between industrial plants. Hence, the value of the current study particularly lies in the fact that it significantly highlights a new dimension through which the practical implementation and operation of eMaintenance can be realized using big data management and data combination approaches.

Effects of the Shaft Normal Modes on the Model-Based Identification of Unbalances in Rotating Machines

2011 ◽  
Author(s):  
A. Vania ◽  
P. Pennacchi ◽  
S. Chatterton
Keyword(s):  
Normal Modes ◽  
Fault Identification ◽  
Diagnostic Strategy ◽  
Rotating Machines ◽  
Large Power ◽  
Identification Methods ◽  
Model Based ◽  
Rotating Machine ◽  
Experimental Response ◽  
Innovative Techniques

Model-based methods can be applied to identify the most likely faults that cause the experimental response of a rotating machine. Sometimes, the objective function, to be minimized in the fault identification method, shows multiple sufficiently low values that are associated with different sets of the equivalent excitations by means of which the fault can be modeled. In these cases, the knowledge of the contribution of each normal mode of interest to the vibration predicted at each measurement point can provide useful information to identify the actual fault. In this paper, the capabilities of an original diagnostic strategy that combines the use of common fault identification methods with innovative techniques based on a modal representation of the dynamic behavior of rotating machines is shown. This investigation approach has been successfully validated by means of the analysis of the abnormal vibrations of a large power unit.

Machine Fault Detection Using Genetic Programming

2005 ◽  
Author(s):  
B. Samanta
Keyword(s):  
Fault Detection ◽  
Genetic Programming ◽  
Vibration Data ◽  
Rotating Machine ◽  
Fault Recognition ◽  
Machine Fault ◽  
Artificial Neural Network Ann ◽  
Monitoring And Diagnostics ◽  
The Time Domain ◽  
Selection Of

Applications of genetic programming (GP) include many areas. However applications of GP in the area of machine condition monitoring and diagnostics is very recent and yet to be fully exploited. In this paper, a study is presented to show the performance of machine fault detection using GP. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GP for two class (normal or fault) recognition. The number of features and the features are automatically selected in GP maximizing the classification success. The results of fault detection are compared with genetic algorithm (GA) based artificial neural network (ANN)- termed here as GA-ANN. The number of hidden nodes in the ANN and the selection of input features are optimized using GAs. Two different normalization schemes for the features have been used. For each trial, the GP and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GP and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers with GP and GA based selection of features.

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.

Integrated Approach for Vibration-Based Condition Monitoring of Rotating Machines

2015 ◽  
Author(s):  
Jyoti K. Sinha
Keyword(s):  
Condition Monitoring ◽  
Experimental Studies ◽  
Integrated Approach ◽  
Rotating Machines ◽  
Rotating Machine ◽  
Research Studies

Conventional Vibration-based Condition Monitoring (VCM) is well known and well accepted in industries to identify the fault(s), if any, in rotating machine since decades. However over the last 3 decades, significant advancement in both computational and instrumentation technologies has been noticed which resulted in number of research studies to find the alternate and efficient methods for fault(s) diagnosis. But most of the research studies may not be leading to an Integrated Modern VCM (IMVCM). It may be because of mainly 2 reasons; (a) the recent proposed methods in the literature are based on numerically simulated studies and a very limited experimental studies and (b) none of the recent studies applied on all kind of faults. In this paper, a summary of a couple of methods proposed and published earlier by author to meet the requirement of the IMVCM is presented.

Cost Effective On-Site Fault Diagnosis Home Appliance Using a Smart Phone and Support Vector Machine

2019 ◽  
Author(s):  
Ji Min Baek ◽  
Kyeong Ha Lee ◽  
Seung Ho Lee ◽  
Ja Choon Koo
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Smart Phone ◽  
Poor Performance ◽  
Cost Effective ◽  
Consumer Electronics ◽  
Support Vector ◽  
Rotating Machines ◽  
Pass Filter ◽  
Rotating Machine

Abstract One of the common rotating machines of the consumer electronics might be a washing machine. The rotating machinery normally suffers mechanical failures even during daily operations that results in poor performance or shortening lifetime of the machine. Therefore, engineers have been interested in the earliest fault diagnosis of the rotating machine. Existing fault diagnosis methods for rotating machines have used fast fourier transform (FFT) method in frequency domain to detect abnormal frequency. However, it is difficult to diagnose using the FFT method if the normal frequency components of the rotating machines overlaps with the fault frequencies. In this paper, sets of acoustic signals generated by the washing machines are collected by using a smart phone in which an inexpensive microphone is equipped, and collected data are analyzed using a new algorithm, which combining the skewness, kurtosis, A-weighting filter, high-pass filter (HPF), and FFT. The analyzed data is applied to support vector machine (SVM) to determine defect existence. The proposed algorithm solves the disadvantages of the existing method and is accurate enough to discriminate the data collected by the cheap microphone of the smart phone.

Diagnosis of rotating machine unbalance using machine learning algorithms on vibration orbital features

2020 ◽  
pp. 107754632092983
Author(s):  
Leonardo S Jablon ◽  
Sergio L Avila ◽  
Bruno Borba ◽  
Gustavo L Mourão ◽  
Fabrizio L Freitas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Machine Learning Algorithms ◽  
Vibration Measurement ◽  
Machine Learning Techniques ◽  
Rolling Element Bearings ◽  
Rotating Machines ◽  
Signal To Noise ◽  
Rotating Machine ◽  
Rolling Element

The diagnosis of failures in rotating machines has been subject to studies because of its benefits to maintenance improvement. Condition monitoring reduces maintenance costs, increases reliability and availability, and extends the useful life of critical rotating machinery in industry ambiance. Machine learning techniques have been evolving rapidly, and its applications are bringing better performance to many fields. This study presents a new strategy to improve the diagnosis performance of rotating machines using machine learning strategies on vibration orbital features. The advantage of using orbits in comparison to other vibration measurement systems is the simplicity of the instrumentation involved as well as the information multiplicity contained in the orbit. On the other hand, rolling element bearings are prevalent in industrial machinery. This type of bearing has less orbital oscillation and is noisier than sliding contact bearings. Therefore, it is more difficult to extract useful information. Practical results on an industry motor workbench with rolling element bearings are presented, and the algorithm robustness is evaluated by calculating diagnosis accuracy using inputs with different signal-to-noise ratios. For this kind of noisy scenario where signal analysis is naturally tough, the algorithm classifies approximately 85% of the time correctly. In a completely harsh environment, where the signal-to-noise ratio can be smaller than −25 dB, the accuracy achieved is close to 60%. These statistics show that the strategy proposed can be robust for rotating machine unbalance condition diagnosis even in the worst scenarios, which is required for industrial applications.

Fault Diagnosis and Prognosis in Rotating Machines Carried Out by Means of Model-Based Methods: A Case Study

2013 ◽  
Author(s):  
A. Vania ◽  
P. Pennacchi ◽  
S. Chatterton
Keyword(s):  
Diagnostic Methods ◽  
Rotating Machines ◽  
Machine Model ◽  
Large Power ◽  
Machine Vibration ◽  
Vibration Data ◽  
Machine Maintenance ◽  
Model Based ◽  
Diagnosis And Prognosis ◽  
Fault Diagnosis And Prognosis

Diagnostic methods, based on mathematical models, can be used to identify the most common faults and malfunctions of rotating machines by minimizing the error between experimental vibration data and the corresponding theoretical response of the rotor system caused by a specific set of excitations. These techniques allow the severity and location of the fault to be estimated. Moreover, depending on the fault characteristics, model-based prognostic techniques can be used to study appropriate corrective actions that can eliminate the cause of the malfunctions or reduce the machine vibration levels. This paper shows the results of a diagnostic analysis carried out to investigate the cause of the high vibration of the HP-IP steam turbine of a large power unit that occurred, during the runups, when approaching the first balance resonance. The numerical results confirmed the suspect that this high vibration was caused by a shaft bow. Then, the machine model was used also to study and optimize a corrective action that allowed the operating speed to be reached and the shaft bow to be eliminated by means of the turbine heating caused by a load rise. The successful results obtained with the machine maintenance carried out considering the indications provided by the model-based diagnostic and prognostic analyses are shown and discussed.

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