Predictive Maintenance Program Research Articles

Implementing Gaussian process modelling in predictive maintenance of mining machineries

Mining machinery constitutes essential assets for a mining corporation. Due to economies of scale, technological innovations and stringent quality and safety requirements, the size, complexity, functionality and diversity of industrial machinery have expanded markedly over the last two decades. This growth has increased sensitivity to machine availability and reliability. Mining operations install comprehensive maintenance units tasked with inspection, repair, replacement and inventory management for the machines in use. Leveraging the proliferation of sensor technologies integrated within the machines, maintenance units obtain rich data streams synchronously disclosing machine health and performance metrics, which enables a predictive maintenance programme. This programme performs prognostic detections of anomalies and permits timely intervention to avert catastrophic breakdowns. However, such sensor-driven predictive maintenance scheme for machinery in the mining sector is limited. The present paper utilises the Gaussian process, a powerful predictive modelling technique, to show its potential in addressing this challenge. The efficacy of this approach is validated through three case studies. Each case study is equipped with sensor data and represents a typical predictive maintenance task for mining assets. The developed Gaussian process models successfully capture meaningful temporal patterns in sensor data and generate credible predictions across all three tasks: temporal prediction of sensor data degradation trends, remaining useful lifespan prediction and simultaneous monitoring and prediction of multiple machine conditions. Furthermore, the models offer uncertainty estimates to the prediction outcomes, potentially facilitating maintenance decision-making process.

Development of Diagnosis System for Rolling Bearings Faults on Real Time Based on FPGA

The real-time monitoring of events in an industrial plant is an advanced technique that presents the real conditions of operation of the machinery responsible for the manufacturing process. A predictive maintenance program includes various rotating machinery condition monitoring techniques of the machine to determine the conditions of failure. To increase the operational reliability and to reduce preventive maintenance, it is necessary an efficient tool for analysis and process monitoring, in real time, enabling the detection of incipient faults for rolling bearings. Over the past few years there has been a major technological developments related to digital system, including innovations in both hardware and software. These innovations enable the development of new design methodologies that take into account the ease of future modifications, upgrades and expansions of the designed system. This paper presents a study of new design tools for embedded digital systems based on open hardware architecture with reconfigurable logic. Will be discussed a case study in the area of fault detection of rolling bearings, as well as its implementation and testing.

Designing a Resilient Building Maintenance Program: Integrating Preventive and Predictive Maintenance at the U.S. Navy

ABSTRACT Building equipment failure can have drastic effects on a company's operations and budget. This paper presents two types of maintenance approaches that if done effectively, can prevent or significantly reduce the failure of building equipment assets. The first is traditional time-based preventive maintenance (PM), which conducts prefailure inspections and tasks in a cyclic time-based approach. The second, is predictive maintenance (PdM), which conducts maintenance functions based on the condition of the equipment found through continuous or cyclic measurements and analysis during machine operation. The purpose of investigating these maintenance approaches is to determine whether we can improve on the U.S. Navy's (Navy) existing facility maintenance program, helping to reduce overall costs while improving sustainability, equipment resiliency, and efficiency. By presenting each maintenance program and leveraging today's technologies we show that these advancements in technology can directly improve the Navy's operational mission and warfighter readiness. Research was conducted through the following methods: interviews, books, third party reports, journal articles, industry websites and articles that focus on equipment maintenance. The Navy, the National Aeronautics and Space Administration (NASA), and the University of Washington provided case studies, existing facility/utility maintenance data, and budget information used in this research. The results show that PdM approaches using advanced analytics are more effective in diagnosing equipment, prescribing equipment problems, and predicting equipment failure. It will also show that when a PdM model is used, building tenants have less operational impacts as equipment operates longer with less downtime between maintenance events. By changing to a PdM program, facility managers and owners can improve asset efficiency and resilience, directly improving environmental sustainability and lowering overall longterm costs. It highlights the significant capital costs of a fully online PdM program and the benefits of using a hybrid model of PM and PdM. This research concludes with an overview of how building maintenance is currently being conducted on Navy bases by Naval Facilities Engineering Systems Command (NAVFAC), and how they are transitioning to a more sustainable maintenance program leveraging existing advanced metering infrastructure (AMI), building control systems (BCS), and utility control systems (UCS) with Smart Grid (SG), OSI Pi, and advanced analytics. In addition, major gaps in this transition are identified, and solutions are proposed to optimize these building system investments.

Implementing Predictive Maintenance (PdM) Programs in Food and Beverage Manufacturing Facilities

Predictive maintenance (PdM) has emerged as a critical approach to maintaining equipment in food and beverage manufacturing facilities. By proactively monitoring and analyzing equipment data, PdM can help to identify and prevent equipment failures before they occur. This can lead to significant reductions in unplanned downtime, improved efficiency, and lower maintenance costs. This paper discusses the key steps involved in implementing a PdM program in a food and beverage manufacturing facility. These steps include: Establishing clear goals and objectives for the PdM program. Identifying critical equipment and collecting data from these assets. Analyzing the collected data to identify patterns and trends that could indicate potential equipment failures. Developing predictive models that can be used to forecast equipment failures. Implementing a process for responding to PdM alerts and taking corrective action. The paper also discusses some of the challenges of implementing a PdM program in a food and beverage manufacturing facility, such as the need for a strong data culture and the need to integrate PdM with other maintenance activities.

Using acoustic emission for condition monitoring of the main shaft bearings in 4-point suspension wind turbine drivetrains

ABSTRACT The continuous growth of wind power technology makes condition monitoring of wind turbine components crucially important for their operational efficiency. The main shaft bearings in wind turbines have been identified as one of the most critical components in the system, especially with the ongoing increase in rotor size and weight. This increase made the 4-point suspension drivetrain more preferable. In this study, we present a novel approach for condition monitoring of the main shaft bearings in a 2 Megawatt wind turbine with 4-point suspension drivetrain using primarily acoustic emission (AE). The focus was on the analysis of time and frequency domains of the AE signal, where the dominant frequency of each AE hit was identified and plotted back in the time domain to create the so-called dominant frequency map in specific time intervals for each bearing. A comparison between the two dominant frequency maps of the two bearings gives valuable insights into the condition of the two bearings. The distinctive nature of the dominant frequency bands in the dominant frequency maps presented promising potential for this method. The presented method is straightforward and can be automated and then integrated into a planned predictive maintenance programme for this wind turbine.

A Methodology for Evaluating and Scheduling Preventive Maintenance for a Thermo-Electric Unit Using Artificial Intelligence

Flow-production systems whose pieces are connected in a row may not have maintenance scheduling procedures fixed because problems occur at different times (electricity plants, cement plants, water desalination plants). Contemporary software and artificial intelligence (AI) technologies are used to fulfill the research objectives by developing a predictive maintenance program. The data of the fifth thermal unit of the power station for the electricity of Al Dora/Baghdad are used in this study. Three stages of research were conducted. First, missing data without temporal sequences were processed. The data were filled using time series hour after hour and the times were filled as system working hours, making the volume of the data relatively high for 2015-2016-2017. 2018 was utilized as a test year to assess the modeling work and validate the experimental results. In the second step, the artificial neural networks approach employs the python program as an AI, and the affinity ratio of real data using the performance measurement of the mean absolute error (MAE) was 0.005. To improve and reduce the value of absolute error, the genetic algorithm uses the python program and the convergence ratio became 0.001. It inferred that the algorithm is efficient in improving results. Thus, the genetic algorithm provided better results with fewer errors than the neural network alone. This concludes that the shown network has superior performance over others and the possibility of its long-term predictions for 2030. A Sing time series helped detect future cases by reading and inferring system data. The development of appropriate work plans will lower internal and external expenses of the systems and help integrate other capabilities by giving correct data sources of raw materials, costs, etc. To facilitate prediction for maintenance workers, an interface has been created that facilitates users to apply them using the python program represented by entering the times, an hour, a day, a month, a year, to predict the type and place of failure.

Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Methodological Framework

Predictive maintenance is considered as one of the most important strategies for managing the utility systems of commercial buildings. This research focused on chilled water system (CWS) components and proposed a methodological framework to build a comprehensive predictive maintenance program in line with Industry 4.0/Quality 4.0 (PdM 4.0). This research followed a systematic literature review (SLR) study that addressed two research questions about the mechanism for handling CWS faults, as well as fault prediction methods. This research rectified the associated research gaps found in the SLR study, which were related to three points; namely fault handling, fault frequencies, and fault solutions. A framework was built based on the outcome of an industry survey study and contained three parts: setup, machine learning, and quality control. The first part explained the three arrangements required for preparing the framework. The second part proposed a decision tree (DT) model to predict CWS faults and listed the steps for building and training the model. In this part, two DT algorithms were proposed, C4.5 and CART. The last part, quality control, suggested managerial steps for controlling the maintenance program. The framework was implemented in a university, with encouraging outcomes, as the prediction accuracy of the presented prediction model was more than 98% for each CWS component. The DT model improved the fault prediction by more than 20% in all CWS components when compared to the existing control system at the university.

A Predictive Maintenance System Design and Implementation for Intelligent Manufacturing

The importance of predictive maintenance (PdM) programs has been recognized across many industries. Seamless integration of the PdM program into today’s manufacturing execution systems requires a scalable and generic system design and a set of key performance indicators (KPIs) to make condition monitoring and PdM activities more effective. In this study, a new PdM system and its implementation are presented. KPIs and metrics are proposed and implemented during the design to enhance the system and the PdM performance monitoring needs. The proposed system has been tested in two independent use cases (autonomous transfer vehicle and electric motor) for condition monitoring applications to detect incipient equipment faults or operational anomalies. Machine learning-based data augmentation tools and models are introduced and automated with state-of-the-art AutoML and workflow automation technologies to increase the system’s data collection and data-driven fault classification performance.

MLC performance prognosis using a degradation model based on trajectory log data from a daily test.

This paper investigates the feasibility of implementing a predictive maintenance program for a multileaf collimator (MLC) based on data collected in trajectory logs (TLs) obtained by conducting a simple daily test, with the aim of minimizing unscheduled downtime. A dynamic field test was designed, and the TLs generated in the course of daily administration in a linear accelerator were collected to evaluate trajectory deviations of the MLC leaves as well as interlocks (COL 420219/20, COL 420207/08) reported by the machine. During this evaluation, we observed that the trajectory deviations of some leaves increased up to a threshold value beyond which certain interlocks began to appear in treatment fields in those leaves. An exponential degradation model was therefore developed to predict this drift and determine each leaf's remaining useful life (RUL). Once the applicability of the model was confirmed, we added a second accelerator equipped with an MLC with the same configuration to validate the model. The model was able to predict certain COL 420219/20 interlocks resulting from primary readout/expected position discrepancies and to estimate each leaf's RUL. In total, 11 cases (8 interlocks + 3 potential interlocks avoided due to service interventions [27.3% of the total]) were detected over 7 days in advance, with no false positive results. Scheduling of service interventions several days prior to MLC failure would therefore be possible. When these types of interlocks were not predicted by the model, they were always generated by leaf motor failure. Consequently, intervention time could also be optimized by directly replacing the motor. During the study period, for these types of interlocks, our approach would have reduced downtime from 35.25 to 4.00h (88.7%) and from 34.75 to 22.83h (34.3%) for each accelerator, respectively. For COL 420207/08 interlocks, which are generated by primary/secondary readout discrepancies, no correlation with leaf trajectory deviation increases recorded in the TLs was found. Throughout the study period, these types of interlocks requiring service intervention, also mainly for motor replacement, represented a downtime of 9.50h for the first accelerator (21.2% of total downtime) and by 4.33h (11.1% of total downtime) for the second accelerator. This study demonstrates that by applying a predictive MLC maintenance program based on information collected in TLs, it is possible to predict certain interlocks and therefore schedule preemptive interventions to avoid their occurrence. This could optimize health-care delivery performance and minimize the loss of treatment sessions.

An improved first-principle model of AC powered solenoid operated valves for maintenance applications

Solenoid operated valves (SOVs) are critical components in many industrial applications. There has been a continuing interest in the industry to have robust condition monitoring, prognostics and health management tools to support the condition based maintenance and predictive maintenance program for such valves. For critical assets like SOVs, it is of paramount interest to understand why a component might be declared as defective. In such a situation, a first principle-model based approach will always be preferred to a purely data-driven approach, because of its inherent interpretability. Furthermore, first principle-models typically have less free parameters than their data driven counterparts and will require less data to identify their parameters. In this paper, we present the improvement of a first-principle model of alternating current (AC) powered SOVs taking into account two important degradation effects. Using this model, we show that the state of degradation can be estimated from current and input voltage measurement signals on the solenoids. Our method is validated using data from an accelerated life test campaign on 48 identical AC-powered SOVs.

Method for predicting dynamic loads for a health monitoring system for subway tracks

This paper presents a method for processing acceleration data registered on a train and determining the health condition of a subway’s substructure. The acceleration data was converted into a dynamic deformation by applying a transfer function defined using the Empirical Mode Decomposition Method. The transfer function was constructed using data produced on an experimental rig, and it was scaled to an existing subway system. The equivalent deformation improved the analysis of the dynamic loads that affect the substructure of the subway tracks because it is considered the primary load that acts on the track and substructure. The acceleration data and the estimated deformations were analyzed with the Continues Wavelet Transform. The equivalent deformation data facilitated the application of a health monitoring system and simplified the development of predictive maintenance programs for the subway or railroad operators. This method better identified cracks in the substructure than using the acceleration data.

Screw Press Machine Damage Analysis with Failure Mode and Effect Analysis (FMEA) Method in Palm Oil Factory PT. Socfindo Seunagan

PT. Socfindo is an industry engaged in the processing of Fresh Palm Fruit Bunches into crude palm oil or CPO (Crude Palm Oil). the day in the morning, therefore it is necessary to identify the presence of machine components that are prone to damage (critical components) and special actions are needed to take special actions against critical parts on the machine, by practicing the Predictive Maintenance Program or prevention. Simulation techniques, often known as Monte Carlo simulations, estimate reliability using random sampling of scenarios. Failure mode and effect analysis (FMEA) is defined as a process used to determine the actions that should be taken to ensure that each physical item or system can run properly according to the intended use by the user. Based on the information obtained, the RPN value of the Rubber Coupling Bush is 18, the RPN value of the Worm Screw is 126, the RPN value of the Press Cage is 72, the Adjusting Cone RPN value is 108, the Tie Rod Nut RPN value is 15, the Drive Shaft RPN value is 168, the Bearing RPN value is 12, the Motor Base RPN value is 15.

MAINTENANCE ANALYTICS FOR BUILDING DECISION-MAKING: A LITERATURE REVIEW

There is a prevalence of poor building maintenance practices in both the public and private sectors in Malaysia. To improve the current state of maintenance, effective decisions must be made by the building stakeholders. Unfortunately, the decision-making process for building maintenance in Malaysia is still traditional. The decisions are usually made based on the latest maintenance inspection without taking into consideration the trend of past data. This limits the building maintenance strategy to corrective (reactive) and preventive (expensive). Data-driven decisions improve building operations and create better predictive maintenance programs because the stakeholders can instantly identify problems and effectively act. Maintenance analytics is a structured and technological approach used to extract information from data and has proven to be an acceptable tool to improve building operation and maintenance. It is used to determine “what has happened?”, “why it happened?”, “what will happen?”, and “what needs to be done?” to enable decision-makers to take appropriate actions. In a country like Malaysia where maintenance practice is not data-driven, there is a need to identify the techniques to improve the maintenance process (especially decision-making). Therefore, this study aims to identify the various analytical techniques applied in existing maintenance analytics studies and determine the current direction of maintenance analytics studies. A comprehensive literature review was done to understand maintenance analytics, types of data and its sources, and the analytical techniques applied. Findings from the literature review revealed that the major data sources are CMMS, BIM, IoT, BAS. It was also noticed that the type of data used influenced the choice of analytical technical techniques. In addition, it was noted that certain studies did not use the major data sources and analytical techniques, and other studies used more than one data source. Overall, the general direction of the maintenance analytics studies was building performance and operation, end-user complaints, and work orders. There is a gap in the application of maintenance analytics to cost-effective decision-making in building maintenance. Which is recommended as the direction for future studies.

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).

Asset/mechanical integrity: A PSM element that still bedevils

AbstractSince the original version of this paper was published at the CCPS conference in 2006, the quality and health of asset integrity/mechanical integrity (AI/MI) programs in the process safety community have improved. However, it continues to be a difficult element to develop and implement. In many respects, it is still the “last frontier” of process safety and has been the last element to be fully addressed in the PSM community. This is not to say that inspection, testing, and preventive maintenance and other related activities that are part of AI/MI programs do not exist at facilities with process safety programs. Preventive and predictive maintenance programs have existed in the chemical/process industry for many years. What has been lacking in some cases are complete integrated AI/MI management system programs that address all of the subelements of AI/MI. There are several reasons for this continuing situation: Understanding the requirements. Which RAGAGEPs are relevant? Common difficulties with mechanical integrity. The impacts of process safety culture.

Dynamic and Friction Parameters of an Industrial Robot: Identification, Comparison and Repetitiveness Analysis

This paper describes the results of dynamic tests performed to study the robustness of a dynamics model of an industrial manipulator. The tests show that the joint friction changes during the robot operation. The variation can be identified in a double exponential law and thus the variation can be predicted. The variation is due to the heat generated by the friction. A model is used to estimate the temperature and related friction variation. Experimental data collected on two robots EFORT ER3A-C60 are presented and discussed. Repetitive tests performed on different days showed that the inertial and friction parameters can be robustly estimated and that the value of the measured joint friction can be used to estimate the unexpected conditions of the joints. Future applications may include sensorless identification of collisions, predictive maintenance programs, or human–robot interaction.

Fault diagnosis through Ferrography (Wear Particle Analysis)

The purpose of Plant Predictive Maintenance (PDM) programme is to improve Reliability of machineries through early detection and diagnosis of equipment problems, and degradation prior to equipment failure. Ferrography (Wear Particle Analysis) is one of the PDM techniques which allows detection, identification and evaluation of the degradation at the very incipient stage so that degradation is timely attended and mitigatory actions initiated. Ferrography is a Wear Particle Analysis technique based upon systematic collection and analysis of sample of lubricating oil from rotating and reciprocating machines. Ferrography analysis is conducted in 2 phases: Stage I – Quantitative, and Stage II – Qualitative. After Stage II analysis, recommendation is issued based on wear rating (Normal, Marginal, or Critical) so that operator can take timely action. Presently, 21 Nuclear Power Plants are operational in India and Forced Shutdown is a very costly affair. Lube oil of around 60 equipment from Indian Nuclear Power Plants is examined quarterly for Ferrography analysis, and failure of several equipment is avoided due to timely action. This paper will elaborate on the basic principles of Ferrography, and how systematic implementation of Ferrography has helped in avoiding forced failure of equipment, and hence prevent Forced Shutdown.

Condition based monitoring for fault detection in windmill gear box using artificial neural network

Gears are condemning element in a diverse industrial application such as machine tool and gearboxes. Many contributing losses occur due to an unexpected breakdown of the gears. Recently, in many researches fault diagonsis has been the most contributing content in the gears. Vibration analysis has been used as a deciding tool for several machinery maintenance decisions. The most efficient form of indication is by the increment in the vibration level. By measuring and analysing the machine’s vibration, it is possible to determine both the nature and severity of the defect, and hence predict the machine’s failure. The vibration signal of a gearbox carries the signature of the fault in the gears and early fault detection of the gearbox is possible by analysing the vibration signal using different signal processing techniques. The present work depicts about the predictive maintenance program implementation based on the experimental data set of the gear box assembly in wind turbines by using the Artificial neural network and comprises of the prediction of variation in the accuracy of the experimental data set. The code values are generated using python 3.7 version software. The accuracy of the Model was found to be 61.86%. This accuracy indicates the functioning of the gears and can also be used for predictions in real time scenario.

Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenance program. Partial discharges (PDs) phenomena affect the insulation system of an electrical machine and—in the long term—can lead to a breakdown, with a consequent, significant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is specifically designed to run on low-cost embedded devices.

Intelligent sensor impact on predictive maintenance program costs

Intelligent sensor impact on predictive maintenance program costs