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The purpose of this paper is to develop the different phases of the implementation of vibration-based maintenance (VBM). Then, the focus will be on the first two stages, namely, the inventory and feasibility study where each step will be translated into a very detailed implementation process through an industrial case study.

The study is based on a state of art on the implementation of the VBM; a survey of national and international experts in the field of VBM and finally an analysis of 30 years of VBM practice in a large Moroccan company in the field of chemical processing, via a collective approach called Diagnostic Court Autonome.

The study showed that improving productivity by reducing downtimes due to vibration defects through effective vibration monitoring is possible and investment in equipment and vibration monitoring personnel is largely justified in the company studied.

This paper presents in detail the two preliminary phases with all procedures describing in a practical way the operating rules to apply and organize the roles of different actors. The work will be useful both to researchers and maintenance managers interested in structuring their vibration monitoring cells.

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.

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.

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.

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.

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.

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.

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.

Discusses the importance of total quality maintenance (TQMain), considering that the common thread in achieving and maintaining high overall equipment effectiveness (OEE) is the availability of improved manufacturing processes capable of producing quality products without interruption. Implementing just‐in‐time (JIT) philosophy demands healthy processes. Any interruption of the line caused by any equipment malfunction or failure will result in a major disruption of output or even line or factory shutdown. Thus, an effective maintenance programme should be evolved to provide the required availability of machinery and output quality. A new concept of condition‐based maintenance, total quality maintenance, is presented. It aims to maintain not only machinery but all the elements constituting a manufacturing process, e.g. production/ operation, environmental condition, personnel, methods, material, quality control. TQMain is a result of establishing a common database through integrating the maintenance program with other plant programs to form an overall plant information technology system. Identifying and “eliminating” quality deviations and failure causes at early stages, and extensive use of data feedback are the suggested tools to accomplish continuous improvements and to assure high quality products.

The purpose of this paper is to develop the steps required for establishing and running cost‐effective condition‐based maintenance (CBM) exemplified for vibration‐based maintenance (VBM) and compared with real implementations in case companies.

The study is based on published knowledge and experience within condition monitoring (CM) and CBM, and three case studies.

The study has shown that all three case companies are in a big need for systematic procedures for doing right things right, in the right time cost‐effectively (establishing and running a cost‐effective CBM).

The paper focuses on development of cost‐effective CBM exemplified for VBM. Also, the case companies belong to three branches: paper and pulp mills, energy producer and engineering manufacturing. The case companies have different levels in applying CBM/VBM, experience and knowledge in CM and CBM.

Systematic procedures for establishing CBM/VBM policy are developed, motivated and discussed in a way which enables the user to establish and run CBM/VBM policy cost‐effectively.

The steps of establishment of CBM/VBM provide a unique model for companies to establish and run cost‐effective maintenance its originality.

The problem addressed in this paper is how to utilise the concept of TQMain to develop an integrated vibration‐based maintenance (VBM) for detecting deviations in the machine condition and product quality (technical specifications) before the machine starts manufacturing defective items. Concepts of TQMain and integrated VBM are discussed. A technical analysis is conducted to identify quality problem cause roots when manufacturing crankshafts in Volvo Motor, Sko¨vde, Sweden. A factorial experiment is conducted to assess the effect and significance of the suspected causes. Vibration measurements from seven points are collected. The major conclusion of this study is: when using an effective, e.g. on the basis of TQMain concept, integrated VBM, deviations in product quality can be detected at an earlier stage than when using usual quality control diagrams. Manufacturing defective items can be reduced dramatically (or prevented), which will eliminate bad quality‐related losses.

The purpose of this paper is to develop a statistical control chart based model for earlier defect identification.

The paper used statistical process control methods and an auto‐regression model to model the identification of the initiation point of a random defect. Conventional statistical process control (SPC) methods have been widely used in process industries for process abnormality detections. However, their practicability and achievable performance are limited due to the assumptions that a continuous process is operated in a particular steady state and that all variables are normally distributed. Because the case considered here does not meet the requirement of conventional SPC methods, we proposed adaptive statistical process control charts based on an autoregressive model to distinguish defects from normal changes in operating conditions. The method proposed has been tested on a set of vibration data of rolling element ball bearings

Several control charts have been used and compared in this paper to identify the initial point of a defect. Overall, the adaptive Shewhart average level chart is a good choice since it overcomes the drawback of adaptive moving charts by working out the limits using all the bearings' data, with no such a need for a subjective threshold level. They are also not very sensitive to the small casual changes in the data.

The model developed can be served as part of a prognosis tool for maintenance decision making since once the earlier warning point has been identified, corrective maintenance actions may be taken. It has practical application areas in vibration based monitoring or any monitoring scheme where a trend in the monitored measurements may exist. The method proposed is easy to use and can be implemented in any condition based maintenance software packages.

The approach proposed in this paper is a new application of existing methods and of original contribution from a point of view of applicability. It adds value to the existing literature and is of value to practitioners.

The purpose of this paper is to present the findings of a recent study conducted with the objective of addressing the problem of failure of baggage carts in the high-speed baggage tunnel at Heathrow Terminal 5 by the development of an innovative condition-based maintenance (CBM) system designed to meet the requirements of 21st century airport systems and Industry 4.0.

An empirical experimental approach to this action research was taken to install a vibration condition monitoring pilot test in the north tunnel at Terminal 5. Vibration data were collected over a 6-month period and analysed to find the threshold of good quality tyres and those with worn bearings that needed replacement. The results were compared with existing measures to demonstrate that vibration monitoring could be used as a predictive model for CBM.

The findings demonstrated a clear trend of increasing vibration velocity with age and use of the baggage cart wheels caused by wheel mass unbalanced inertia that was transmitted to the tracks as vibration. As a result, preventative maintenance is essential to ensure the smooth running of airport baggage. This research demonstrates that a healthy wheel produces vibration of under 60 mm/s whereas a damaged wheel measures up to 100 mm/s peak to peak velocity and this can be used in real-time condition monitoring to prevent baggage cart failure. It can also run as an autonomous system linked to AI and Industry 4.0 airport logic.

Whilst vibration monitoring has been used to measure movement in static structures such as bridges and used in rotating machinery such as railway wheels (Tondon and Choudhury, 1999); this is unique as it is the first time it has been applied on a stationary structure (tracks) carrying high-speed rotating machinery (baggage cart wheels). This technique has been patented and proven in the pilot study and is in the process of being rolled out to all Heathrow terminal connection tunnels. It has implications for all other airports worldwide and, with new economic sensors, to other applications that rely on moving conveyor belts.

In the case of machines, structures and assemblies, the crack generation and propagation is becoming a great concern, especially in airplane wings, turbine blades and such other applications. This is because these parts are very large in size and the crack size is very small, i.e. in microns. Hence, there is an important need to locate the crack and to find its severity before it starts to propagate and also to detect these parameters by on-site non-destructive testing methods. This paper aims to develop and test the methodology to locate an unknown single open crack in steel cantilever beam along with its severity.

This study covers analytical, numerical and experimental analysis for healthy and cracked beams. Vibration-based approach and finite element analysis (FEA) approach is used for analytical and numerical study respectively. Own designed and dedicated experimental set-up is used for testing purpose along with fast fourier transform analyzer. An anti-resonance technique is used to locate and to find the severity of unknown crack. The statistical approach helps to validate the results.

The comparison of the natural frequency of healthy and cracked steel cantilever beam shows that the crack in the beam reduces its natural frequency. The accuracy of results is achieved by finding actual density and Young's modulus of steel specimen under consideration. It is helpful to verify the health of the non-cracked beam by applying dye testing. The study of natural frequency and anti-resonance gives the location of crack and its depth also. The FEA approach proved to be an important tool for numerical analysis of cracked beam.

The research is limited to steel material and surface cracks only.

Practically, this study highlights how to locate a surface crack in steel beam along with its depth, i.e. severity with great accuracy. Identification of the factors such as location and depth of a crack provide the severity of damage in airplane wings, turbine blades, bridges and many more, and thereby, it helps in safety at working vicinity.

The identification and solutions of current research helps to predict the operational life of machine elements such as airplane wings, turbine blades, bridges and many more, and thereby, it helps in the safety of people in working vicinity of such structures.

The work presented, is based on original research and experimentation. This work is valued contribution in the field of methodologies applied for fault detection in structures and also determining its correctness by numerical and experimental work.

The purpose of this paper is to provide an overview of research and development in the measurement of maintenance performance. It considers the problems of various measuring parameters and comments on the lack of structure in and references for the measurement of maintenance performance. The main focus is to determine how value can be created for organizations by measuring maintenance performance, examining such maintenance strategies as condition‐based maintenance, reliability‐centred maintenance, e‐maintenance, etc. In other words, the objectives are to find frameworks or models that can be used to evaluate different maintenance strategies and determine the value of these frameworks for an organization.

A state‐of‐the‐art literature review has been carried out to answer the following two research questions. First, what approaches and techniques are used for maintenance performance measurement (MPM) and which MPM techniques are optimal for evaluating maintenance strategies? Second, in general, how can MPM create value for organizations and, more specifically, which system of measurement is best for which maintenance strategy?

The body of knowledge on maintenance performance is both quantitatively and qualitatively based. Quantitative approaches include economic and technical ratios, value‐based and balanced scorecards, system audits, composite formulations, and statistical and partial maintenance productivity indices. Qualitative approaches include human factors, amongst other aspects. Qualitatively based approaches are adopted because of the inherent limitations of effectively measuring a complex function such as maintenance through quantitative models. Maintenance decision makers often come to the best conclusion using heuristics, backed up by qualitative assessment, supported by quantitative measures. Both maintenance performance perspectives are included in this overview.

A comprehensive review of maintenance performance metrics is offered, aiming to give, in a condensed form, an extensive introduction to MPM and a presentation of the state of the art in this field.

The purpose of this paper is to review the literature on maintenance management and suggest possible gaps from the point of view of researchers and practitioners.

The paper systematically categorizes the published literature and then analyzes and reviews it methodically.

The paper finds that important issues in maintenance management range from various optimization models, maintenance techniques, scheduling, and information systems etc. Within each category, gaps have been identified. A new shift in maintenance paradigm is also highlighted.

Literature on classification of maintenance management has so far been very limited. This paper reviews a large number of papers in this field and suggests a classification in to various areas and sub areas. Subsequently, various emerging trends in the field of maintenance management are identified to help researchers specifying gaps in the literature and direct research efforts suitably.

The paper contains a comprehensive listing of publications on the field in question and their classification according to various attributes. The paper will be useful to researchers, maintenance professionals and others concerned with maintenance to understand the importance of maintenance management