Condition-based Maintenance Program Research Articles

Enhancing the Effective Performance of Critical Equipment’s In Sugar Industry

Abstract: The sugar industry has undergone significant changes in the past decade, with increased competition, rising costs and greater equipment complexity, while budgets, cane crushing days, operating margins, and maintenance staff have decreased. Any unexpected breakdowns of plant machineries can have a major effect on the availability and productivity of the whole plant. As a process industry, any failure in critical equipment can result in extended downtime, which directly affects the revenue generation of the entire plant. In this context, the maintenance department needs to demonstrate a positive impact on the sugar factories bottom line and necessitating the development or implementation of quality maintenance systems. A comprehensive condition-based maintenance program should incorporate a variety of technologies. While vibration monitoring is typically a key component of most condition-based maintenance programs, other monitoring and diagnostic techniques should also be included to provide a comprehensive approach in avoiding the unplanned sugar plant stoppages during crushing season.

Statistical Machine Learning Strategy and Data Fusion for Detecting Incipient ITSC Faults in IM

The new technological developments have allowed the evolution of the industrial process to this new concept called Industry 4.0, which integrates power machines, robotics, smart sensors, communication systems, and the Internet of Things to have more reliable automation systems. However, electrical rotating machines like the Induction Motor (IM) are still widely used in several industrial applications because of their robust elements, high efficiency, and versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions; hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs, and its appearance is due to electrical stresses leading to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology capable of performing the assessment and automatic detection of incipient electric faults like ITSC in IMs; the proposed method is supported through the processing of different physical magnitudes such as vibration, stator currents and magnetic stray-flux and their fusion of information. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as their fusion following a feature-level fusion approach and their reduction through the Linear discriminant Analysis technique. Furthermore, the fusion and reduction of information from different physical magnitudes lead to performing automatic fault detection and identification by a simple Neural-Network (NN) structure since all considered conditions can be represented in a 2D plane. The proposed method is evaluated under a complete set of experimental data, and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) can lead to achieving a global classification ratio of up to 99.4% during the detection of ITSC in IMs and an improvement higher than 30% in comparison with classical approaches that consider the analysis of a unique physical magnitude. Additionally, the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

Optimal condition based maintenance using attribute Bayesian control chart

Condition-based maintenance (CBM) has been emerged as a relatively new trend in maintenance management. Instead of conducting preventive maintenance actions in specified time intervals, the CBM program collects information through condition monitoring, then recommends maintenance actions based on the observed data. On the other hand, Bayesian control charts use the posterior probability of being the system in an unhealthy state as the chart statistic. An attribute Bayesian control chart is employed in this study to monitor a deteriorating system and plan CBM actions based on a continuous-time homogeneous Markov chain. The system consists of three states: healthy, unhealthy, and failure states. A partially observable Markov decision process (POMDP) is developed, which optimally determines the sample size, sampling interval, and warning limit to minimize the long-term expected cost per time unit. Numerical examples and sensitivity analyses are conducted to clarify the performance of the proposed attribute control chart. To the best of the authors’ knowledge, this is the first study of the applications of attribute Bayesian control charts in condition-based maintenance.

Simulating dynamic RUL based CBM scheduling. A case study in the railway sector

This paper uses continuous time computer simulation tools to analyze the maintenance scheduling problem of a fleet of assets that is subjected to a CBM (Condition-Based Maintenance) program for critical components under a maintenance 4.0 environment. Detection of component anomalies, their diagnosis and prognosis are considered as built-in capabilities of the organization. Once the remaining useful life (RUL) of a component at risk is known, the organization must react and determine when the component’s on-condition maintenance can be released. Maintenance on-condition activities are released controlling a variable named accumulated excess of anomalies over the CBM capacity constraint. When the existing CBM capacity is not enough to service all the components with their lowest possible RUL, components could be replaced with a higher RUL, as few times as possible, to ensure the largest component´s lifecycle. The paper explores the implications of different CBM capacity levels modelling a cost function. Cost function factors considered are cost of lost RUL, cost of CBM capacity, cost of overdue CBM and cost of asset unavailability due to overdue CBM. Empirically, the paper shows how capacity can be optimized to minimize this cost function. Once all different possibilities to schedule CBM activities are modeled, together with the cost of the selected CBM strategy, the paper compare results with those obtained for a base case where the organization could detect anomalies in components but not schedule CBM activities according to their RUL limitations and the maintenance organization capacity constraints. The paper demonstrates the different benefits of this opportunistic CBM task scheduling, according to assets stops for their predetermined PM activities. The tool that is developed has been tested in the railway sector, for a fleet of trains. Interesting results are obtained for different strategies, and they are discussed to understand possible implications of changes in the different factors and parameters of the problem.

Probabilistic Fatigue Damage Estimation for Rotorcraft Life-Limited Components

Condition-based maintenance programs for modern helicopters rely on algorithmic techniques to estimate the useful life remaining for life-limited components. Regime-recognition-based condition-based maintenance programs involve a regime recognition step and a damage estimation step in which damage is calculated based on the identified regimes. Recently, new probabilistic regime recognition algorithms have been developed that produce probability distributions over the regimes, rather than deterministic regime classifications. However, to date, there has been no method to convert regime distributions to damage estimates. This paper proposes a technique to compute a probability distribution over the fatigue damage for each life-limited component directly from the regime probability distributions. The method treats the incurred damage at a given time as a random variable and accumulates the total damage incurred as a sum of random variables. The damage distribution at each time is computed from the regime distribution and the regime damage rates. A primary advantage of the approach is that it captures uncertainty in the regime recognition process by treating damage as a random variable rather than a deterministic value. Simulation results illustrate the benefit of the probabilistic approach over a deterministic method, particularly for flights where there is significant uncertainty in the flown regimes.

International Journal of Prognostics and Health Management, ISSN 2153-2648, 2017 019 1 A Condition Based Maintenance Implementation for an Automated People Mover Gearbox

Data-driven condition-based maintenance (CBM) can be an effective predictive maintenance strategy for components within complex systems with unknown dynamics, nonstationary vibration signatures or a lack of historical failure data. CBM strategies allow operators to maintain components based on their condition in lieu of traditional alternatives such as preventive or corrective strategies. In this paper, the authors present an outline of the CBM program and a field pilot study being conducted on the gearbox, a critical component in an automated cable-driven people mover (APM) system at Toronto’s Pearson airport. This CBM program utilizes a paired server-client “two-tier” configuration for fault detection and prognosis. At the first level, fault detection is performed in real-time using vibration data collected from accelerometers mounted on the APM gearbox. Time-domain condition indicators are extracted from the signals to establish the baseline condition of the system to detect faults in real-time. All tier one tasks are handled autonomously using a controller located on-site. In the second level pertaining to prognostics, these condition indicators are utilized for degradation modeling and subsequent remaining useful life (RUL) estimation using random coefficient and stochastic degradation models. Parameter estimation is undertaken using a hierarchical Bayesian approach. Degradation parameters and the RUL model are updated in a feedback loop using the collected degradation data. While the case study presented will primarily focus on a cable-driven APM gearbox, the underlying theory and the tools developed to undertake diagnostics and prognostics tasks are broadly applicable to a wide range of other civil and industrial applications.

Remaining Useful Life Estimation by Stochastic Markov Model and Monte-Carlo Simulation

Estimation of remaining tool life is important in the planning of condition based maintenance program and helped in preventing any production loss. In this paper, a method has been proposed to esti...

Innovative digital inspection methodology

The paper describes an innovative digital inspection methodology that combines 3D laser scanning, metrology and advanced non-destructive testing data that is merged in 3D space to provide a digital record of the condition and mechanical integrity of critical assets. This advanced inspection method supports condition-based maintenance programs and digital twin models to determine future equipment condition, work scope and inspection schedules, while maintaining a digital record throughout the equipment lifecycle. Testing of the methodology includes 3D scanning of drill platforms, baseline scanning of blowout preventers and sheaves, for quality purposes, and the use of augmented reality for viewing scans. Phased array testing has been conducted on sub-components such as slew ring bolting. Data are combined into digital reports that show 3D images of the equipment with precise dimensional data and identified inspection areas. Such reports can be combined with digital twin models to confirm integrity of the equipment for certificate of conformance and baseline data for future integrity comparisons as equipment ages. This innovative inspection methodology will set a new standard for how equipment data are captured, stored and represented. The process provides a range of benefits for OEMs, drilling contractors and operators alike, including digital quality programs to baseline new equipment condition and compare with design parameters, delivering condition and integrity assessments of critical equipment items in-situ or on deck, providing a consistent methodology for inspection and dimensional control of operational equipment items, and providing precise equipment data that can complement digital twin and real time monitoring programs.

Multi Equipment Condition Based Maintenance Optimization Using Multi-Objective Evolutionary Algorithms

Thanks to the digitalization of industry, maintenance is a trending topic. The amount of data available for analyses and optimizations in this field has increased considerably. In addition, there are more and more complex systems to maintain, and to keep all these devices in proper conditions, which requires maintenance management to gain efficiency and effectiveness. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, but often these programs are complex to manage and understand. The problem becomes more complex when equipment is analyzed in the context of a plant, where equipment can be more or less saturated, critical regarding quality, etc. Thus, this paper focuses on CBM optimization of a full industrial chain, with the objective of determining its optimal values of preventive intervention limits for equipment under economic criteria. It develops a mathematical plus discrete-event-simulation based model that takes the evolution in quality and production speed into consideration as well as condition based, corrective and preventive maintenance. The optimization process is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case, where the data gathered by the IoT (Internet of Things) devices at edge level can detect when some premises of the CBM model are no longer valid and request a new simulation. The simulation performed in a centralized way can thus obtain new optimal values who fit better to the actual system than the existing ones. Finally, these new optimal values can be transferred to the model whenever it is necessary. The approach developed has raised the interest of a partner of the Deusto Digital Industry Chair.

Data-driven CBM tool for risk-informed decision-making in an electric arc furnace

Nowadays, maintenance activities and safety management can be supported by a mature state of the art favouring the implementation of condition-based maintenance programme, which recommends maintenance decisions based on the information collected through asset life. The main idea, which grounds in the Industry 4.0 paradigm, is to utilize the asset degradation information, extracted and identified through different techniques, to reduce and eliminate costly, unscheduled downtimes and unexpected breakdowns and to avoid risky scenarios. This paper aims at developing and testing a data-driven CBM tool to provide fault diagnostics transforming raw data from the shop-floor into information, finally enabling risk-informed decision-making. The tool relies on a process of knowledge discovery that incorporates both prior knowledge and proper interpretation of data analytics results. Prior knowledge is extracted through a process hazard analysis (PHA), while data analysis deals with statistical process control and novelty detection. The model is proposed to integrate some Cyber-Physical System element in the extant plant automation, to exploit its computational capabilities through the continuous monitoring and data analytics. This enables a “watchdog agent” of risky scenario, allowing an on-line risk-assessment of safety-critical components, finally enhancing the intelligence in the industrial process.

Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms

Maintenance has always been a key activity in the manufacturing industry because of its economic consequences. Nowadays, its importance is increasing thanks to the “Industry 4.0” or “fourth industrial revolution”. There are more and more complex systems to maintain, and maintenance management must gain efficiency and effectiveness in order to keep all these devices in proper conditions. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, even though often these programs are complex to manage and understand. For this reason, several research papers propose approaches that are as simple as possible and can be understood by users and modified by experts. In this context, this paper focuses on CBM optimization in an industrial environment, with the objective of determining the optimal values of preventive intervention limits for equipment under corrective and preventive maintenance cost criteria. In this work, a cost-benefit mathematical model is developed. It considers the evolution in quality and production speed, along with condition based, corrective and preventive maintenance. The cost-benefit optimization is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case.

Joint optimization of monitoring quality and replacement decisions in condition-based maintenance

The quality of condition monitoring is an important factor affecting the effectiveness of a condition-based maintenance program. It depends closely on implemented inspection and instrument technologies, and eventually on investment costs, i.e., a more accurate condition monitoring information requires a more sophisticated inspection, hence a higher cost. While numerous works in the literature have considered problems related to condition monitoring quality, (e.g., imperfect inspection models, detection and localization techniques, etc.) few of them focus on adjusting condition monitoring quality for condition-based maintenance optimization. In this paper, we investigate how such an adjustment can help to reduce the total cost of a condition-based maintenance program. The condition monitoring quality is characterized by the observation noises on the system degradation level returned by an inspection. A dynamic condition-based maintenance and inspection policy adapted to such a observation information is proposed and formulated based on Partially Observable Markov Decision Processes. The use and advantages of the proposed joint inspection and maintenance model are numerically discussed and compared to several inspection-maintenance policies through numerical examples.

Condition Based Maintenance Program for Combined Cycle Power Plant

Combined cycle power plant plays an important role to generate electricity in an efficient way. The greater efficiency achievement is due to the reason that the exhaust heat energy from the gas turbine units is being utilized to produce steam for the steam turbine units. It is also true that the entire mechanical system becomes more complex than that of a single gas turbine or steam turbine plant. Therefore, it necessitates taking care of this complex system properly so that availability of the plant can be achieved to a great extent. This availability is closely related to the maintenance activity of the plant. Since preventive maintenance policy incurred significant involvement of cost, it justifies going for condition-based maintenance (CBM). Prognostic and diagnostic are the main two aspects of CBM. Prognostic is more important than diagnostic as prognostic can show the trend of abnormality inside the equipment of the plant. However, diagnostic is also important if abnormality has already been achieved. Based on this requirement, the present work has been chosen where condition monitoring has been performed for a combined cycle power plant. Used lubricating oils have been collected from the different units of the plant at a regular interval for three years and the collected samples have been experimented to investigate of wear rate of the equipment. Ferrographic technique has been applied to examine wear situation. Results indicate that it is required to implement CBM policy for the plant.

Cutting Tool Prognostic using Markov Model

Objectives: Prognosis of a tool is essential for assigning a proper Condition-based Maintenance program for it. Therefore the objective of the present study is to investigate the reliability of a tool using a stochastic Markov Model. Methods/ Statistical Analysis: This work proposes a stochastic Markov model for estimating the Remaining Useful Life of the turning tool. In this study, a Mild Steel workpiece was machined to a certain length on a lathe machine using a high-speed steel tool and the Flank Wear Width (FWW) were recorded in every 20 minutes interval. This experiment was conducted for stable feed, stable speed and uniform depth of cut until the failure value of the tool flank wear was achieved, i.e. 0.3 mm. Findings: A state based model is developed considering four different degraded stages of the tool. The degradation rates among the states are obtained from the recorded experimental data. The appropriate equations for the four-state Markov model were derived, which show the possibilities of physical changes in the context of time for each level. The set of equations is solved analytically in MATLAB software using Range-Kutta method. After solving these equations, it was concluded that this system is 43% reliable for a 300-minute period and 41% is reliable for 500 minutes time period. Application/Improvements: It helps in preventing any kind of production loss. The remaining lifespan of a tool can be predicted by carefully analyzing the data gathered from the trend exploration method such as health monitoring with time. Keywords: Condition based Maintenance, Cutting Tool, Markov Model, Prognosis, Remaining Useful Life

Multiple failure behaviors identification and remaining useful life prediction of ball bearings

Accurate remaining useful life (RUL) prediction is the key for successful implementation of condition based maintenance program in any industry. Data driven prognostics approaches are generally used to predict the RUL of the components. Presence of noise in the data reduces the accuracy of RUL prediction. Mechanical components are prone to failures due to several failure modes; resulting into multiple failure behaviors or patterns in life test data obtained from various units. If such failure patterns or behaviors are not identified and treated appropriately, the same may act as one of the sources for data noise. In the present research, clustering and change point detection algorithm (CPDA) is used for identification of the presence of multiple failure behaviors in the data. Silhouette width value is used to find out the optimum number of clusters. Combined output of clustering and CPDA is used for developing RUL prediction models. Separate models for single and multiple failure behaviors are constructed using General Log-Linear Weibull (GLL-Weibull) distribution. Results show that identification of failure behavior helps in accurate prediction of RUL. The approach is validated using roller ball bearing life test data.

Multi-branch hidden Markov models for remaining useful life estimation of systems under multiple deterioration modes

Remaining Useful Life (RUL) estimation plays an important role in implementing a condition-based maintenance (CBM) program, since it could provide sufficient time for maintenance crew to act before an actual system failure. This prognostic task becomes harder when several deterioration mechanisms co-exist within the same system due to the variability and dynamics of its operating environment, since the RUL obviously depends on the mode that the system is following. In this paper, we propose a multi-branch modeling framework to deal with such problems. The proposed model consists of several branches in which each one represents a deterioration mode and is considered as a hidden Markov model. The system’s conditions are modeled by several discrete meaningful states, such as “good”, “minor defect”, “maintenance required” and “failure”, which would be easy to interpret for maintenance personnel. Furthermore, these states are considered to be “hidden” and can only be revealed through observations. These observations are the condition monitoring information in the CBM context. The performance of the proposed model is evaluated through numerical studies. The results show that the multi-branch model can outperform the standard one-branch HMM model in RUL estimation, especially when the “distance” between the deterioration modes is considerable.

A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies

CBM (Condition Based Maintenance) solutions are increasingly present in industrial systems due to two main circumstances: rapid evolution, without precedents, in the capture and analysis of data and significant cost reduction of supporting technologies. CBM programs in industrial systems can become extremely complex, especially when considering the effective introduction of new capabilities provided by PHM (Prognostics and Health Management) and E-maintenance disciplines. In this scenario, any CBM solution involves the management of numerous technical aspects, that the maintenance manager needs to understand, in order to be implemented properly and effectively, according to the company’s strategy. This paper provides a comprehensive representation of the key components of a generic CBM solution, this is presented using a framework or supporting structure for an effective management of the CBM programs. The concept “symptom of failure”, its corresponding analysis techniques (introduced by ISO 13379-1 and linked with RCM/FMEA analysis), and other international standard for CBM open-software application development (for instance, ISO 13374 and OSA-CBM), are used in the paper for the development of the framework. An original template has been developed, adopting the formal structure of RCM analysis templates, to integrate the information of the PHM techniques used to capture the failure mode behaviour and to manage maintenance. Finally, a case study describes the framework using the referred template.

Condition-based Maintenance: Determination of optimal deterioration levels to perform preventive activities by using a multi-objective evolutionary algorithm

Several authors insist on the fact that maintenance is a key activity in the manufacturing industry, because of its economic consequences. Within maintenance, Condition-Based Maintenance programs can provide significant advantages to industrial plants. This paper is focused on the problem of Condition-Based Maintenance optimization in an industrial environment, with the objective of determining both the critical age level to perform preventive maintenance activities and the amount of this type of activities to be executed before upgrading or substituting components. For this purpose, a mathematical model who jointly considers the evolution in quality and production speed along with condition based, corrective and preventive maintenance is presented. The cost and profit optimization process using a Multi-Objective Evolutionary Algorithm is applied to an industrial case.

Joint optimization of condition-based maintenance and production lot-sizing

Due to the development of sensor technologies nowadays, condition-based maintenance (CBM) programs can be established and optimized based on the data collected through condition monitoring. The CBM activities can significantly increase the uptime of a machine. However, they should be conducted in a coordinated way with the production plan to reduce the interruptions. On the other hand, the production lot size should also be optimized by taking the CBM activities into account. Relatively fewer works have been done to investigate the impact of the CBM policy on production lot-sizing and to propose joint optimization models of both the economic manufacturing quantity (EMQ) and CBM policy. In this paper, we evaluate the average long-run cost rate of a degrading manufacturing system using renewal theory. The optimal EMQ and CBM policy can be obtained by minimizing the average long-run cost rate that includes setup cost, inventory holding cost, lost sales cost, predictive maintenance cost and corrective maintenance cost. Unlike previous works on this topic, we allow the use of continuous time and continuous state degradation processes, which broadens the application area of this model. Numerical examples are provided to illustrate the utilization of our model.

Cost-benefit Analysis of Prognostics and Condition-based Maintenance Concepts for Commercial Aircraft Considering Prognostic Errors


 
 
 This paper provides a lifecycle cost-benefit analysis of the use of Prognostics and Health Management (PHM) systems in future or present commercial aircraft. The approach considers individual aircraft component’s failure behavior, prognostic performance levels including prognostic errors, and condition-based maintenance (CBM) concepts. The proposed methodology is based on a discrete-event simulation for aircraft operation and maintenance and uses an optimization algorithm for the planning and scheduling of condition-based maintenance (CBM) tasks. In the study, a 150-seat short-/medium-range aircraft equipped with PHM and subject to a CBM program is analyzed. The simulation results are evaluated from an operational and economic perspective. The analysis results can support the derivation of technical and economic requirements for prognostic systems and CBM planning concepts.