Concept Of Predictive Maintenance Research Articles

Criticality Analysis of A Sea Port`s Shore Cranes Using Analytic Hierarchy Process Method

Introduction/Background Critical assets are the organizational resources of crucial importance for maintaining operations and achieving the organization's mission. The criticality of organization assets is continuously researched by numerous authors. Ports have critical importance for the global supply chain. Together with the port workforce, port infrastructure, and port superstructure, which are interconnected with the port information system, port machinery enables the functioning of a port. Depending on the characteristics of a port (level of its specialization, etc.), there are various port machinery types used in the cargo handling process, and all of them do not have the same criticality level. In the available literature, no research directly related to the criticality analysis of shore cranes in use at a terminal in a seaport can be found. It was one of the principal initial motives of the author to write this paper. Methodology After an overview of literature sources dealing with assets` criticality analysis and a condensed theoretical consideration of the general importance of ports and port machinery, this paper shows the results of research on the port`s shore cranes‘ criticality ranks, using Analytic Hierarchy Process (AHP) method. The research included a group of shore cranes at the Dry Bulk Cargo Terminal in the Port of Bar JSC (Montenegro), which consists of three gantry cranes (with a capacity per item of 12 t), two mobile harbor cranes with a capacity of 144 t and 124 t and a ship loader for cereals with a capacity of 300 t/h. Criticality analysis was conducted based on four selection criteria: C1-if the shore crane type is an element of cargo handling technology with priority cargo group; C2-level of hourly productivity; C3-level of operating costs; C4-level of cost generated by shore crane downtime during the loading/unloading process. The AHP hierarchy framework used was a three-level model: “level 0” – goal; “level 1” the – criterion; “level 2” the – choice. Results/Discussion According to the calculated level of criticality based on the defined selection criteria, the highest criticality rank was observed for the mobile harbor crane with a capacity of 144 t, which had the highest rank for three out of four selection criteria. Conclusion The results of the research can be used as a base for further research studies in this field as well as a reliable base for a very wide range of managerial activities directed to improvements in the maintenance system of shore cranes as well as in the cargo handling system where the cranes are used. As one of the potential purposes of the results, with a high level of importance, their potential usage for introducing, adequate modeling, and implementing the concept of predictive maintenance of shore cranes (with all related benefits) is pointed out.

Scalable electromagnetic energy harvester for wind turbine rotor blade applications

One of the biggest challenges in structural health monitoring for rotor blades in wind turbines is to provide enough energy to power wireless sensor nodes. Batteries are not an adequate solution due to their limited lifetime and conventional cabling fails due to the rotation of the rotor blade. Therefore, we present an electromagnetic energy harvester that is specifically designed to be operated inside rotor blades and can generate a sufficient amount of energy. It uses the changing gravitational force vector to move a permanent magnet in a tube and converts this mechanical into electrical energy by coils arranged around the tube. Finite element methods simulations were performed to estimate the generated energy and an extensive parameter sweep of several key design parameters provided guidance for an optimized performance of a prototype. This device was characterized in the lab followed by a field test in a wind turbine where it was operated for several days and provided a continuous and rectified power of 6 mW, enough to power conventional wireless accelerometers, typically used within a predictive maintenance concept for the vibrational monitoring of rotor blades.

INTRODUCTION OF THE «PREDICTIVE MAINTENANCE» CONCEPT TO INCREASE RELIABILITY AND PREDICTABILITY OF REPAIRS

This research article discusses the importance of implementing the Predictive Maintenance concept in the context of rocket and artillery weapons to improve the reliability and predictability of repair work. The article discusses key aspects of this concept, including its basic principles and methods, including the use of data analytics and artificial intelligence. The authors analyze the benefits of Predictive Maintenance in the military context, pointing to reduced repair costs, increased availability of military equipment, and extended equipment life. In addition, the article examines the challenges that arise when implementing this concept and provides recommendations on how to overcome them. Modern military equipment requires a high level of reliability and combat readiness. The introduction of the Predictive Maintenance concept is becoming extremely important to ensure the efficiency and safety of military equipment. This research article is devoted to the study and discussion of the possibilities of implementing Predictive Maintenance in the context of rocket and artillery weapons in order to increase the reliability and predictability of repair work. In conclusion, the article emphasizes the importance of implementing Predictive Maintenance as a strategic tool to ensure optimal performance and readiness of missile and artillery weapons in the modern military environment. Keywords: Implementation, concept, Predictive Maintenance, reliability, predictability, repair work, data analytics, artificial intelligence, monitoring systems, machine learning algorithms, sensors, failure prediction, planning, efficiency, readiness, rocket and artillery weapons.

Fusion and Comparison of Prognostic Models for Remaining Useful Life of Aircraft systems

Changes in the performance of an aircraft system will straightforwardly affect the safe operation of the aircraft, and the technical requirements of Prognostics and Health Management (PHM) are highly relevant. Remaining Useful Life (RUL) prediction, part of the core technologies of PHM, is a cutting-edge innovation being worked on lately and an effective means to advance the change of upkeep support mode and work on the framework's security, unwavering quality, and economic reasonableness. This paper summarizes a detailed preliminary literature review and comparison of different prognostic approaches and the forecasting methods' taxonomy, the methodology's details, and provides its application to aircraft systems. It also provides a brief introduction to the predictive maintenance concept and condition-based maintenance (CBM). This article uses several predictive models to predict RUL and classifies conventional regression algorithms according to the similarity in function and form of the algorithms. More classical algorithms in each category are selected to compare the prediction results, and finally, the combined effects of the RUL prediction are obtained by weighted fusion, accuracy, and compatibility. The performance of the proposed models is assessed based on evaluations of RUL acquired from the hybrid and individual predictive models. This correlation depends on the most current prognostic metrics. The outcomes show that the proposed strategy develops precision, robustness, and adaptability. Hence, the work in this paper shall enrich the advancement of predictive maintenance and modern innovation of prognostic development.

An Industry 4.0 implementation of a condition monitoring system and IoT-enabled predictive maintenance scheme for diesel generators

In most business and residential organizations, Diesel Generators (DG) is a viable supplementary power source for ensuring an undisturbed power supply. The DG is a hybrid machine that generates electrical energy using a Diesel Engine (DE) and an Electric Generator (EG). By routinely monitoring crucial machine parameters, alternative power source efficiency can be improved. Furthermore, Condition Monitoring Systems (CMS) based on the Internet of Things (IoT) have supplanted the traditional equipment maintenance method. Predictive maintenance is also an important building block of Industry 4.0, whose entire process and performance can be fully understood by using IoT-enabled Remote Monitoring (RM) schemes. Firstly, this paper introduces a remote monitoring and data acquisition scheme to realize the concept of predictive maintenance. Secondly, this article discusses a strategy for real-time observation of DG parameters as well as a comprehensive analysis of various metrics. Thirdly, this research article includes a monitoring and analysis scheme of crucial factors in a DG, like the speed of an engine, voltage output, the current produced, power factor, coolant required, fuel consumption, and battery health. Different mathematical models are formulated by correlating experimental data and estimating the coefficients. Finally, to create suitable real-time warnings under critical circumstances, a fuzzy logic-based Decision Support System (DSS) and web-based integration elements are presented.

Multiphase Flowmeter Health Monitoring Strategy: Maximizing the Value of Real-Time Sensors and Automation for Industrial Revolution 4.0

Summary Metering with multiphase flowmeters (MPFMs) is a well-proven intelligent field technology, which utilizes complex flow models to accurately measure multiphase flow (oil, gas, and water) on a real-time basis. The major realized benefits of utilizing MPFM real-time sensor data technology are reduced maintenance costs, optimized maintenance schedules, and improved safety via minimizing visits and predicting well performance. MPFMs also have the capability to perform comprehensive real-time test validation in a short period of time, making them a valuable tool. Leveraging their capabilities, engineers can perform various production analysis scenarios remotely using continuous data streaming to reduce and ultimately eliminate reliance on operators and vendors in remote fields. Nevertheless, such MPFMs require frequent maintenance, which could be quite difficult to manage when having a large number of meters. An in-house application was developed to support those objectives and to remotely monitor the health of all MPFMs on a real-time basis. The in-house analytics system comprises four modules: Field Overview, MPFM Overview, Well Test Management, and Alarm Management. The steps used to avail this kind of solution cover infrastructure requirements, visualization requirements, optimization, and prediction. The solution was designed as the major interface for engineers to proactively detect and prevent major MPFM component failures and highlight all types of projected MPFM component failures. It proactively harvests preset alarms, which leads to preventing internal MPFM parts from failing. These alarms are generated based on critical parameters measured by the MPFM such as rates, differential pressure (DP), and radioactive measurements. Annual savings are realized through use of the monitoring solution by minimizing costly offshore visits and optimizing the visits based on problematic MPFMs. The authors will discuss a newly developed MPFM health monitoring strategy based on real-time solutions, driving migration from a preventative to a predictive maintenance concept to reduce costs and optimize manpower time by eliminating unnecessary visits.

Прогнозуюче технічне обслуговування –важлива сфера застосування комп’ютерних систем

Predictive maintenance is a widely applied maintenance program that requires extensive support of computer aided systems. The program uses specific procedures that are to be addressed when developing predictive maintenance software solutions. Despite the fact that software solutions for predictive maintenance were introduced almost at the same time as the program emerged, it still remains a very actual field for the application of computer aided systems. The practice also shows that developers of computer aided systems for predictive maintenance constantly encounter problems, trying to translate predictive maintenance procedures into the computer language. These procedures are very specific and require microprocessor-based equipment and development of sophisticated algorithms. Therefore, there is a distinct need for better awareness about the predictive maintenance concept among software developers. The article aims to describe the essence of the predictive maintenance concept, its fundamental approaches and the main physical processes upon which the predictive maintenance procedures are based: (1) distinct vibration frequency components which are inherent in all common failure modes; and (2) constant amplitude of each distinct vibration component. The importance of the awareness with the concept for computer aided systems developers is emphasized. And the most problematic areas of software application in predictive maintenance programs are outlined, namely the development of effective computerized systems to capture and analyze an immense quantity of data (big data processing), and the development of systems, supporting an intelligent connection of smart devices with the means of internet protocols (Internet of Things).

Analytical research of artificial intelligent models for machining industry under varying environmental strategies: An industry 4.0 approach

Since the introduction of Industry 4.0, manufacturing industries have adopted smarter automation systems enabling better interconnection amongst various aspects of the production industry. Application of industry 4.0 furnishes better performance and efficiency with improved reliability and robustness. The present research provides a novel framework which takes in consideration the complexity and flexibility of the working environment within the factory premises, previously not explored. Smart systems equipped with sensors and communicators are responsible for monitoring information and detecting malfunctions pre-hand which eventually boosts the system performance. Furthermore, the research explores the concept of predictive maintenance in industry 4.0 setup which apprehends any system failure based on atmospheric related changes. A novel algorithm is explored in this research which takes in consideration multisource diverse dataset based on varying environmental conditions and simultaneously furnishing inputs for predictive maintenance in Industry 4.0 implementation, thereby providing a transparent and effective manufacturing method. The framework for Industry 4.0 is validated and deemed feasible with quantitative comparison with previous prediction models which can further predict any future malfunctions in the industrial machines. The productivity values are validated with models developed with the help of intelligent hybrid prediction techniques such as adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM). The input parameters considered are atmospheric conditions whereas the required output response is productivity of the machines. Error rates were evaluated lowest error rate for triangular membership functions for both machining models.

Intelligent Diagnosis of Equipment Health Based on IOT and Operation Large Data Analysis

Predictive maintenance integrates equipment condition monitoring, fault diagnosis, fault prediction, maintenance decision support and maintenance activities. Intelligent manufacturing upgrades need to match the synchronous improvement of predictive maintenance capabilities, and predictive maintenance is the basic guarantee for enterprises to achieve intelligent manufacturing. Take the equipment condition monitoring and diagnosis system applicate in process industry as an example, this paper proposes IOT and operation big data analysis to equipment fault monitoring, diagnosis and preventive maintenance. Under the three-layer system framework of perception layer, network layer and application layer, machine learning algorithm is applied to carry out data mining on the equipment operation big data, establish expert knowledge base, obtain diagnosis rules, and realize the intelligent and efficient management mode integrating online monitoring, remote monitoring, remote diagnosis, fault matching and identification. Based on IOT and operation large data analysis, equipment health intelligent diagnosis provides the basic guarantee for equipment intelligent operation and maintenance, which helping enterprise establish new equipment management, maintenance, inspection and repair system under the concept of predictive maintenance and proactive maintenance.

Detecting leakage current by infrared thermography method

<span>An alternating current leakage can happen in electrical installation such as switchgear panel. If it’s not being detected earlier and address properly, it might lead to unintended incident or accident such as fire, electric shock, and power supply trip. Occasionally, if it appears, tracing its root cause can be difficult. The conventional method is by isolating one by one of the electrical loads with aids of multimeter and clamp meter. <span class="apple-converted-space"> </span>Unfortunately, this conventional method could be a tedious job for installations involved in numbers of electrical panels. Therefore, an alternative method is deeming necessary. This paper describes research works on Infrared Thermography (IRT) as a new method in detecting leakage current as aids in resolving related electrical problems. The scope of study mingling around to determine IRT’s suitable parameters that represent the identification of leakage current, derive new mathematical equation correlating leakage current to IRT and conduct experiment accordingly. Two new equations derived which are leakage current relationship to thermogram and infrared net radiation power. The results of experiment adherence to the hypothesis drawn. Consequently, helps to realize predictive maintenance concept by detecting the root cause of leakage current ahead its triggering points of unintended incident and accident.</span>

Mechanical fault detection in electric motors measured by a digital signal processing device in an optical mouse

The objective of this work is to study the application of a new methodology for diagnosing mechanical faults in electric motors through vibration analysis and using a non-contact approach based on a digital signal processing (DSP) device in an optical computer mouse. The mechanical fault used to test the proposed methodology was related to bearing damage. An experimental bench was set up to test the detection of damage in bearings. The analyses of the measured signals were performed in both time and frequency domains. Pattern recognition methods were used to classify the bearing state as damaged or not damaged. The electric motor was tested with two sets of bearings: a new set and a damaged set. The results demonstrated that the proposal for the prediction and diagnosis of faults was an efficient and promising technique because it is non-destructive and non-invasive due to the absence of contact between the sensor and the motor. Based on the concept of predictive maintenance, the proposed method has the potential to become an efficient and low-cost tool for predicting failures.

The Predictive Maintenance Concept in the Maintenance Department of the “Industry 4.0” Production Enterprise

Abstract Modern technical environments require a high degree of reliability both in machinery and in equipment. Technological progress has, on the one hand, increased this efficiency but on the other hand, it has changed the way in which this equipment and these machines have traditionally been maintained. The authors have set the following assumptions. In order to survive in the market and develop, modern production enterprises realize the assumptions of Industry 4.0, wherein the optimization of maintenance processes is important because of the financial situation. This includes the profits made by the production company and differs from traditional maintenance, by shifting towards new trends such as predictive maintenance; as such, it is crucial for the development of the company. The article is devoted to the most modern predictive maintenance strategy, in the maintenance department of a manufacturing company. The publication describes the meaning of the method, its potential and the theory of action.

Smart and cognitive solutions for Operator 4.0: Laboratory H-CPPS case studies

This paper refers to the physical, sensing, and cognitive interaction in the Industry 4.0 concept and presents Human Cyber-Physical Production Systems in a laboratory environment. The Industry 4.0 defines new demands for productivity and effectiveness in production, which inevitably influences operators as well. A traditional operator is being transformed to the Operator 4.0. In this paper, we describe evolving roles of the operators in the factories. There are many ways to enhance the operator’s physical, sensing, and cognitive capabilities. Given case studies are based on the defined Operator 4.0 typology. Types can be used individually or in combination to put humans into the center of technological revolution. We also describe our laboratory case studies developed during the previous research. We have focused on legacy systems, healthcare monitoring platform, the concept of predictive maintenance, and machine to people interaction system based on operator position. These case studies illustrate the possible applications of smart and cognitive solutions for the Operator 4.0. The obtained results are then evaluated by a set of criteria to compare legacy systems with our laboratory case studies based on the Operator 4.0 concept.

Abrasive wear based predictive maintenance for systems operating in sandy conditions

Machines operating in sandy environments are damaged by the abrasive action of sand particles that enter the machine and become entrapped between components and contacting surfaces. In the case of the military services the combination of a sandy environment and the wide range of tasks to be fulfilled results in extreme and uncertain operating conditions. All of this hinders the ability to establish efficient maintenance strategies prior to deployment and increases the risk of mechanical failure. To prevent such problems, it would be desirable to perform maintenance based on the prevailing condition of both the components and the environment. By monitoring the loading situation as well as the characteristics of the sand particles, the wear of components is quantified, allowing maintenance to be performed when necessary.The development and implementation of such a predictive maintenance concept requires knowledge of the operational and environmental conditions and how they relate to the principal wear mechanisms. Based on previously established relationships between the abrasive particles and the resulting abrasive wear, the current work focuses on the implementation of these results into a predictive maintenance concept for vehicles that operate in a sandy environment. For this, the local parameters that govern the wear mechanism, such as the normal forces and sliding distances need to be linked to machine usage parameters including the type of terrain and the driving distance.The proposed concept is demonstrated using a case study on the sprockets of a military vehicle, where the sprockets wear progressively during use of the vehicle due to the abrasive action of sand. The predictive maintenance concept is shown to support the determination of maintenance intervals under a range of usage profiles and sand varieties.

Prognostics and Health Management (PHM) - State-of-the-art in Predictive Maintenance

With the growing advancement in sensor technology and automated controls in engineering systems, trend of on-board data recording has greatly increased. The data analysis tools along with the real-time sensor data, have resulted in a paradigm shift in the way systems are maintained. After the industrial revolution, the first generation of maintenance philosophy was corrective maintenance, in which, components or systems were fixed once they got failed. In safety critical systems, this maintenance philosophy produced catastrophic results. The next generation of maintenance philosophy was preventive maintenance, in which maintenance was performed based on the manufacturer's or OEM specified life. Although, this maintenance philosophy has been able to achieve better results in terms of safety, however, in perspective of life-cycle costs, preventive maintenance philosophy is wasteful. Therefore, a few decades back, the concept of predictive maintenance became popular. Ever since it came into being, predictive maintenance has gone through several evolutionary stages. Reliability centered maintenance or RCM, makes calculated estimates of maintenance schedules based on the archived statistical data from the field. With the development in MEMS sensor technology, Condition-Based Maintenance (CBM) using real-time data has become the prevalent maintenance philosophy. The recent advances in data processing technologies have enabled us to determine not only the present health but also to predict the future health of systems. In other words, system's health is can not only be estimated but health can also be managed. Therefore, another phase of predictive maintenance, that is Prognostics and Health Management (PHM) has evolved. In PHM, two key modules are fault diagnostics and failure prognostics. In fault diagnostics, the current health of the system using sensor data is estimated, while in failure prognostics future evolution of health is predicted. This lecture consists of a review of fundamental concepts of fault diagnostics / failure prognostics, which will be followed by the tools and techniques used to develop PHM algorithms. The lecture also includes a review of the state-of-the-art in this field. A few case studies related to fault detection / prediction in aircraft engine, electromechanical and electrochemical domains will also be discussed.

Dynamic Characterization and Predictive Maintenance Concept of Machine Tool Spindle

Vibration analysis has long been used for detection and identification of the machine tool dynamic condition. Predictive maintenance is directed towards recognizing the earliest significant changes in machinery condition. In contrast with the protective condition monitoring in which fast response is the primary requirement, predictive monitoring is not limited by time and may use a greater range of complex characteristics. The main focus is to identify a procedure in order to obtain natural frequencies values for the machine tool spindle using tracking dynamic analysis.

Prognostics applied to aircraft line maintenance: Brake wear prediction based on Bayesian Networks

Abstract The aeronautics industry is attempting to implement important changes to its maintenance strategy. The corrective and preventive maintenance practise is evolving to predictive maintenance to minimize the cost of the maintenance support and to increase operational reliability and availability. The main objective of the article is to show the new predictive maintenance concept and the usefulness of Bayesian networks in supporting the intelligent function “decision support”. This function provides specific models for the prediction of degradation in some of the critical components of the aircraft, the basis for the new maintenance strategy concept based on prognosis. In addition, the article discusses the development of the Bayesian network model for the specific case of predicting brake wear.

New Horizons on Predictive Maintenance

This paper deals with predictive maintenance and its importance on industrial applications. After introduction, brief information is given about causes of electrical motor failures and then, predictive maintenance concept is introduced. Next, most popular techniques are told briefly. Model based fault detection technique which is utilized by Artesis MCM (Motor Condition Monitor) unit is mentioned. A case study is given at the end of the paper.

Importance of early detection of maintenance problems in rotating machines in management of plants: Case studies from wire and tyre plants

In this article, importance of application of predictive maintenance concept with vibration measurement has been discussed. Appreciation of the subject was performed with the help of data collected cases related to maintenance problems from Plants A and B. The first case was examined in aerial conductor manufacturing Plant A. The company does not apply predictive maintenance (PM) in management of the manufacturing activities. The machines used in conductor and similar wire processing plants have very big rotors and their loads on rotors continuously change. Therefore, these types of machines are good examples in checking the health of the machine regularly. The second case was analysed in vehicle tyre plant named as Plant B and it has perfect usage ability of vibration technique of PM methods. The importance of the regular collection of vibration signatures in evaluation of conditions of rotating machines has been analysed in details to indicate the power of the predictive maintenance technique in prevention of losses, which are caused by unbalanced forces, misalignments, improper lubrication of ball bearings, metal fatigue and cracks occurred in welding of constructed parts and locking of the ball bearings due to excessive heating. The second case observed in Plant B is an example to show detection of a ball bearing failure just in time. Evaluation of second case has been performed using measurements of vibration signatures of the machines with the help of fault indicator graphics. Results from the cases have been summarised to give useful ideas in management of the plants to the persons responsible with production and maintenance subjects.

Murphy's Lawyers

This article focuses on the concept of predictive maintenance (PdM) and its use in the manufacturing industry. Predictive maintenance is an umbrella term covering techniques such as vibration analysis, lube oil evaluation, and thermography. Despite the progress in automation, communication, and data manipulation that has allowed PdM companies to monitor tons of machines efficiently and accurately, the technology has not displaced people from the picture altogether. Companies are finding it more cost-effective to funnel the data to the experts instead of maintaining their own experts on site. The turbine generator at the University of North Carolina co-generation plant in Chapel Hill provides electricity to the campus and hospital. At the highest level, where a machine’ failure could severely affect a plant’s safety, environment, or profit, a full arsenal of PdM weapons, including periodic or continuous vibration monitoring, lube oil analysis, and infrared thermography, would keep its eyes on things. In another example discussed in the article, a reliability-centered approach aligns a plant’s business objectives and maintenance strategy to decide upon the best way to monitor assets.