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.

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.

scholarly journals Information system for epidemic control: a computational solution addressing successful experiences and main challenges

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Guilherme Conceição Rocha ◽  
Henrique Mohallem Paiva ◽  
Davi Gonçalves Sanches ◽  
Daniel Fiks ◽  
Rafael Martins Castro ◽  
...  
Keyword(s):  
Information System ◽  
Lessons Learned ◽  
Contact Tracing ◽  
Practical Implementation ◽  
Automatic Identification ◽  
Technical Solution ◽  
Epidemic Control ◽  
Content Type ◽  
Contamination Risk ◽  
Health Authorities

PurposeThe SARS-CoV-2 pandemic has caused a major impact on worldwide public health and economics. The lessons learned from the successful attempts to contain the pandemic escalation revealed that the wise usage of contact tracing and information systems can widely help the containment work of any contagious disease. In this context, this paper investigates other researches on this domain, as well as the main issues related to the practical implementation of such systems and specifies a technical solution.Design/methodology/approachThe proposed solution is based on the automatic identification of relevant contacts between infected or suspected people with susceptible people; inference of contamination risk based on symptoms history, user navigation records and contact information; real-time georeferenced information of population density of infected or suspect people; and automatic individual social distancing recommendation calculated through the individual contamination risk and the worsening of clinical condition risk.FindingsThe solution was specified, prototyped and evaluated by potential users and health authorities. The proposed solution has the potential of becoming a reference on how to coordinate the efforts of health authorities and the population on epidemic control.Originality/valueThis paper proposed an original information system for epidemic control which was applied for the SARS-CoV-2 pandemic and could be easily extended to other epidemics.

scholarly journals Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1394 ◽  
Author(s):  
Akilu Yunusa-Kaltungo ◽  
Ruifeng Cao
Keyword(s):  
Failure Modes ◽  
Large Data ◽  
Processing Parameters ◽  
Machine Learning Techniques ◽  
Rotating Machines ◽  
Smart Systems ◽  
Vibration Data ◽  
Learning Techniques ◽  
Faults Diagnosis ◽  
Manual Classification

Rotating machines are pivotal to the achievement of core operational objectives within various industries. Recent drives for developing smart systems coupled with the significant advancements in computational technologies have immensely increased the complexity of this group of critical physical industrial assets (PIAs). Vibration-based techniques have contributed significantly towards understanding the failure modes of rotating machines and their associated components. However, the very large data requirements attributable to routine vibration-based fault diagnosis at multiple measurement locations has led to the quest for alternative approaches that possess the capability to reduce faults diagnosis downtime. Initiatives aimed at rationalising vibration-based condition monitoring data in order to just retain information that offer maximum variability includes the combination of coherent composite spectrum (CCS) and principal components analysis (PCA) for rotor-related faults diagnosis. While there is no doubt about the potentials of this approach, especially that it is independent of the number of measurement locations and foundation types, its over-reliance on manual classification made it prone to human subjectivity and lack of repeatability. The current study therefore aims to further enhance existing CCS capability in two facets—(1) exploration of the possibility of automating the process by testing its compatibility with various machine learning techniques (2) incorporating spectrum energy as a novel feature. It was observed that artificial neural networks (ANN) offered the most accurate and consistent classification outcomes under all considered scenarios, which demonstrates immense opportunity for automating the process. The paper describes computational approaches, signal processing parameters and experiments used for generating the analysed vibration data.

Faults Diagnosis in Rotating Machines Using Higher Order Spectra

2014 ◽  
Author(s):  
Akilu Yunusa-Kaltungo ◽  
Jyoti K. Sinha
Keyword(s):  
Signal Processing ◽  
Condition Monitoring ◽  
Transverse Crack ◽  
Higher Order ◽  
Vibration Response ◽  
Rotating Machines ◽  
Vibration Signals ◽  
Faults Diagnosis ◽  
Higher Order Spectra ◽  
Recent Attention

Higher order spectra (HOS) and higher order coherences (HOC) are two classes of higher order signal processing tools that have gained recent attention in the area of rotating machines’ condition monitoring (CM). Hence, the current study compares and presents the results of the performances of both HOS and HOC in the diagnosis of rotating machines’ faults, through the numerically simulated vibration signals and the experimentally measured vibration response on a rotating rig with healthy condition and the rotor with a transverse crack.

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 Introducing Lean Six Sigma to a German municipality: an action research report

2018 ◽  
Vol 9 (2) ◽  
pp. 221-237 ◽  
Author(s):  
Ingo Kregel ◽  
André Coners
Keyword(s):  
Action Research ◽  
Public Sector ◽  
Six Sigma ◽  
Lean Six Sigma ◽  
Lessons Learned ◽  
Research Report ◽  
Practical Implementation ◽  
Communication Management ◽  
Content Type ◽  
The Public

Purpose This paper aims to expand the knowledge about Lean Six Sigma (LSS) implementation in the public sector. By analyzing an LSS improvement initiative in a German municipality, examples of success, barriers and challenges are discussed. A comparison with literature regarding the production and service sectors unfolds similarities and differences. Design/methodology/approach The paper applies the action research method. Especially for the broad field of project management, methods focusing on actual experience from practice have been recommended by many researchers. Findings Implementations of LSS in the public sector seem to be particularly challenging and lengthy. Change and communication management have proved to be the most important aspects to successful acceptance, cooperation and improvement sustainability. In the analyzed cases, the needed volume of data could often not be procured. The applied Six Sigma methodology primarily included the DMAIC project phases as well as selected standard instruments. In contrast, the lean elements of LSS achieved more results and were appreciated by project team members. Originality/value The LSS application in this paper provides insights into practical implementation experience in a municipality, as well as lessons learned. Until now, most research addressed the single application of lean, continuous improvement or Six Sigma. This paper represents the first academic report of a LSS program in a German municipality and underlines the need for scientific support of those initiatives in further municipalities worldwide.

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.

Bispectrum: A Tool for Distinguishing Different Faults in Rotating Machine

2012 ◽  
Author(s):  
Keri Elbhbah ◽  
Jyoti K. Sinha
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
State Of The Art ◽  
Ball Bearings ◽  
Rotating Machines ◽  
Rotating Machine ◽  
Current State ◽  
The Future ◽  
Experimental Rig ◽  
Initial Results

The current state-of-the-art in vibration-based condition monitoring of rotating machines requires a number of vibration transducers at each bearing pedestal of a rotating machine to identify any faults, in the machine. In this paper, the use of the bispectrum has been proposed for fault diagnosis in rotating machines. The reason for this is that it may reduce the number of vibration transducers at each bearing pedestal in rotating machines in the future. The paper presents a comparison of the bispectrum results for four cases, namely; Healthy, Misaligned shaft, Crack Shaft and Shaft Rub on an experimental rig consisting of two rigidly coupled shafts supported through 4 ball bearings. Only one accelerometer has been used for this purpose at each bearing and the initial results observed are encouraging.

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