Condition-based Maintenance Strategy Research Articles

A framework for the practical development of condition monitoring systems with application to the roller compactor

Implementing a condition-based maintenance strategy requires an effective condition monitoring (CM) system that can be complicated to develop and even harder to maintain. In this paper, we review the main complexities of developing condition monitoring systems and introduce a four-stage framework that can address some of these difficulties. The framework achieves this by first using process knowledge to create a representation of the process condition. This representation can be broken down into simpler modules, allowing existing monitoring systems to be mapped to their corresponding module. Data-driven models such as machine learning models could then be used to train the modules that do not have existing CM systems. Even though data-driven models tend to not perform well with limited data, which is commonly the case in the early stages of pharmaceutical process development, application of this framework to a pharmaceutical roller compaction unit shows that the machine learning models trained on the simpler modules can make accurate predictions with novel fault detection capabilities. This is attributed to the incorporation of process knowledge to distill the process signals to the most important ones vis-à-vis the faults under consideration. Furthermore, the framework allows the holistic integration of these modular CM systems, which further extend their individual capabilities by maintaining process visibility during sensor maintenance.

Multiple-failure mode division and condition-based maintenance decision making for systems with multi-indicator performance degradation

In prognostics and health management research, it is typically assumed that the health status of a system can be reflected by a single performance indicator. However, modern industrial systems are complex and often require multiple indicators to represent different aspects of system health. Furthermore, these indicators may be associated with multiple fault types, leading to multiple failure modes. This study focuses on systems with multi-indicator performance degradation. We construct a division model of multiple failure modes, and investigate the optimal condition-based maintenance decision under multiple failure modes. By constructing division models for systems with two-indicator performance degradation and systems with three-indicator performance degradation, a common multiple-failure mode division model for systems with multi-indicator performance degradation is to distinguish different fault types that may occur in such systems. Taking the systems with two-indicator performance degradation as an example, a condition-based maintenance strategy is developed that considers the differences in maintenance types and effectiveness corresponding to different faults types. A joint probability recursive model under the influence of strategies is derived, and a cost-rate model in a finite time horizon is established to determine the optimal inspection cycle and maintenance threshold for each indicator. Taking the steel rolling system as an application case, the correctness and effectiveness of the proposed strategy and model are verified. The results indicate that the division model can represent the complex relationships among multiple indicators and fault types and that the strategy considering multiple maintenance types and effects can reduce the cost rate of the system.

Condition-based opportunistic maintenance strategy for multi-component wind turbines by using stochastic differential equations

The components of wind turbines are complex in structure and the working environment is harsh, which makes wind turbines face problems such as high failure rates and high maintenance costs. In this paper, the stochastic differential equation model has been established for the harsh operating environment of wind turbines, and used Brownian motion to simulate random disturbances; aiming at the problem of high failure rate of wind turbines, based on Weibull distribution, a new model has been established by combining operating time and equipment state to calculate the failure rate; in the analysis of monitoring data, the Higher-Order Moment method and Bayesian method were used to solve the parameters. The opportunity maintenance threshold curve and preventive maintenance threshold curve were obtained by analyzing Time-Based Maintenance and Condition-Based Maintenance. Therefore, the Condition-Based Opportunistic Maintenance strategy was obtained. The effectiveness of the proposed method was finally verified by arithmetic examples.

Integration of Industry 4.0 to the CBM practices of the O&G upstream sector in Nigeria

Purpose This study investigates the integration of Industry 4.0 (I4.0) technologies with condition-based maintenance (CBM) in upstream oil and gas (O&G) operations, focussing on developing countries like Nigeria. The research identifies barriers to this integration and suggests solutions, intending to provide practical insights for improving operational efficiency in the O&G sector.Design/methodology/approach The study commenced with an exhaustive review of extant literature to identify existing barriers to I4.0 implementation and contextualise the study. Subsequent to this foundational step, primary data are gathered through the administration of carefully constructed questionnaires targeted at professionals specialised in maintenance within the upstream O&G sector. A semi-structured interview was also conducted to elicit more nuanced, contextual insights from these professionals. Analytically, the collected data were subjected to descriptive statistical methods for summarisation and interpretation with a measurement model to define the relationships between observed variables and latent construct. Moreover, the Relative Importance Index was utilised to systematically prioritise and rank the key barriers to I4.0 integration to CBM within the upstream O&G upstream sector.FindingsThe most ranked obstacles in integrating I4.0 technologies to the CBM strategy in the O&G industry are lack of budget and finance, limited engineering and technological resources, lack of support from executives and leaders of the organisations and lack of competence. Even though the journey of digitalisation has commenced in the O&G industry, there are limited studies in this area.Originality/valueThe study serves as both an academic cornerstone and a practical guide for the operational integration of I4.0 technologies within Nigeria's O&G upstream sector. Specifically, it provides an exhaustive analysis of the obstacles impeding effective incorporation into CBM practices. Additionally, the study contributes actionable insights for industry stakeholders to enhance overall performance and achieve key performance indices (KPIs).

Bi-objective flexible job shop scheduling on machines considering condition-based maintenance activities

This paper investigates multi-objective flexible job shop scheduling considering maintenance activities. The condition-based maintenance strategy is used to reduce machines breakdown. After maintenance activities are completed, the machine degradation model’s parameters are updated using Bayesian inference to make it more realistic. A novel multi-objective evolutionary algorithm is designed to address the multi-criteria scheduling problem. An innovative insertion algorithm is proposed in this paper to balance production plan and maintenance activities. According to the experimental results, the designed evolutionary algorithm performs better than the other two traditional algorithms, and the insertion approach can reduce the impact of maintenance activities on production plan by up to 200%.

Improving the reliability of power distribution substations equipment for life extension by considering Condition Based Maintenance

Maintenance of aged power equipment is a proven technique for establishing systems reliability and longevity. It is extensively relevant when combined with Condition Based Maintenance (CBM) approaches for predicting power system infrastructure life, especially with imminent obsolescence. The research focuses on distribution companies with mixed aged power infrastructure, which continuously degradate. A significant setback in older systems is the scarcity of replacement parts leading to unplanned investment in substantial infrastructure due to unexpected failed components that are no longer in production. To guarantee excellent infrastructure health in older equipment, utility companies should consider applying Condition Based Maintenance (CBM) using Artificial Neural Network (ANN) to stabilize older equipment and restore malfunctioning assets. Studies have shown that the life span of older equipment can be extended when adequate maintenance strategies are put in place through the prediction of possible failures using equipment inspections and risk matrix. CBM's strategy improved the Key Performance Indicator (KPIs) System Average Interruption Duration Index (SAIDI) has improved by lowering the average monthly customers power interruptions by 19,15 % lesser time spent without electricity. The System Average Interruption Frequency Index (SAIFI) was improved by lowering the average monthly power interruptions frequency by 20,63% lesser outage frequency. The Distribution Supply Loss Index (DSLI) was improved by dropping the average monthly power losses by 7,5% power losses saved. The Customers Average Interruption Duration Index (CAIDI) was improved by lowering the average customer interruption by 36.1% improvement from the average period before the CBM optimization. The Average System Availability Index (ASAI) was improved by shortening the average annual interruption durations by 1,36%. The ASUI was improved by increasing the average duration of electrical power availability by 1.28%. The optimization of CBM was measured over 24 moths period.

Research on imperfect condition-based maintenance strategy based on accelerated degradation process

To address the situation that the degraded system cannot be repaired as new and the degradation is accelerated after repair in engineering practice, an imperfect condition-based maintenance model is proposed based on the Gamma process by introducing the maintenance improvement factor and the accelerated degradation factor. The maintenance improvement factor describes the recovery degree of the degradation amount after imperfect maintenance, then gives a quantitative representation method of the system maintenance improvement effect and the determination method of the maximum number of imperfect maintenance. The accelerated degradation factor is combined with the geometric process to describe the accelerated degradation process of the system after imperfect maintenance. The Monte Carlo method is used to determine the optimal number of imperfect preventive maintenance decisions. Through the demonstration of calculation examples, the influence of accelerated degradation factor and maintenance improvement factor on the long-term operation maintenance rate and the optimal number of imperfect maintenance is analyzed. The results show that the model can more comprehensively describe the degradation and maintenance process of degraded systems, and it can effectively reduce maintenance cost ratio under long-term operation. It provides a certain theoretical support for formulating an economical and reasonable maintenance strategy that is more in line with the actual degradation and maintenance process in engineering practice.

A novel maintenance strategy for manufacturing system considering working schedule and imperfect maintenance

Condition-based maintenance (CBM) strategies that consider product quality lack a thorough analysis of the impact of manufacturing enterprise’s working schedule, which does not align with real-life production scenarios. To address this gap, this study proposes a novel CBM strategy model for manufacturing systems that takes into account working schedule to determine the optimal balance between maintenance and production while considering product quality. The maintenance set includes imperfect maintenance and perfect maintenance to capture real-life manufacturing scenarios. The model establishes an optimal maintenance strategy using a Markov decision process (MDP) framework. A numerical study is conducted based on internal information and data collected by sensors from a commercial vehicle manufacturing enterprise to demonstrate the effectiveness of the proposed strategy. We proposed a dual-value iteration algorithm to solve the MDP model. Finally, a comprehensive sensitivity analysis is performed to investigate the impact of cost-related parameters on the strategy.

Impact of agile, condition-based maintenance strategy on cost efficiency of production systems

Maintenance plays an increasingly important role in the life of production companies, as professional maintenance is an important prerequisite for the reliable operation of resources. A well-chosen maintenance strategy can make a major contribution to increased efficiency of production processes. The main goal of this research is to propose a novel optimization approach to define optimal maintenance strategy that ensures the efficient operation of the production process while reducing maintenance costs. The developed optimization method is based on Howard’s policy iteration and describes the objective of the planning as a Markov decision process. The novelty and the scientific contribution of the presented study is the application of Howard’s policy iteration methodology in a Markov decision process for agile, condition-based maintenance strategy optimization. As the results of the numerical analysis of the scenarios shows, the implementation of an optimized maintenance strategy based on the proposed approach can significantly increase the maintenance efficiency of the production process. The main reason for this is that the level and type of maintenance is always implemented depending on the current state of the system components, which reduces both the maintenance cost and the losses due to production downtime.

AI-Enabled Condition Monitoring Framework for Outdoor Mobile Robots Using 3D LiDAR Sensor

An automated condition monitoring (CM) framework is essential for outdoor mobile robots to trigger prompt maintenance and corrective actions based on the level of system deterioration and outdoor uneven terrain feature states. Vibration indicates system failures and terrain abnormalities in mobile robots; hence, five vibration threshold classes for CM in outdoor mobile robots were identified, considering both vibration source system deterioration and uneven terrain. This study proposes a novel CM approach for outdoor mobile robots using a 3D LiDAR, employed here instead of its usual use as a navigation sensor, by developing an algorithm to extract the vibration-indicated data based on the point cloud, assuring low computational costs without losing vibration characteristics. The algorithm computes cuboids for two prominent clusters in every point cloud frame and sets motion points at the corners and centroid of the cuboid. The three-dimensional vector displacement of these points over consecutive point cloud frames, which corresponds to the vibration-affected clusters, are compiled as vibration indication data for each threshold class. A simply structured 1D Convolutional Neural Network (1D CNN)-based vibration threshold prediction model is proposed for fast, accurate, and real-time application. Finally, a threshold class mapping framework is developed which fuses the predicted threshold classes on the 3D occupancy map of the workspace, generating a 3D CbM map in real time, fostering a Condition-based Maintenance (CbM) strategy. The offline evaluation test results show an average accuracy of vibration threshold classes of 89.6% and consistent accuracy during real-time field case studies of 89%. The test outcomes validate that the proposed 3D-LiDAR-based CM framework is suitable for outdoor mobile robots, assuring the robot’s health and operational safety.

Bayesian Network Training Method to Produce a Condition-Based Maintenance Strategy in Aviation Maintenance Programs

With an understanding of the current industry and organization orientation, the aviation maintenance industry is preparing a new paradigm shift toward a CBM (Condition-Based Maintenance) strategy. However, one challenge the aviation maintenance industry faces is the lack of CBM training support in the current education setting. This study aims to fill the gap in the CBM strategy training in current aviation maintenance programs. The authors propose Condition-Based Maintenance Bayesian Network (CBM-BN) training materials. The BN has a different principal approach than other frequentist principles, which can generate a prediction model concerning all heterogeneous information. In this paper, the authors describe a framework to develop CBM-BN training material that can be performed in aviation maintenance education. The proposed CBM-BN framework has probability concepts and ten steps; each step has three sections, including materials, activities, and examples for instructors and students. The case study demonstrates that the developed CBM-BN framework and training materials could facilitate CBM strategy training in aviation maintenance programs. A mechanic who can do CBM analysis will be more beneficial and demandable in the job market and contribute to full CBM implementation. Moreover, other CBM educational materials would be needed to compensate for the limitations of BN and increase the maturity level of CBM.

Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines

Through condition-based maintenance strategy, engineers can monitor the health states of equipment and take actions based on the sensor data. Limited by the low failure frequency and high monitoring costs, it is difficult to obtain sufficient historical data of all fault types for condition monitoring (CM). In the steam turbine operation, environmental factors, varying power consumption and manual adjustments can lead to a multimode process, which consists of multiple normal and abnormal conditions. This paper proposes a framework for online unsupervised CM and anomaly detection, not relying on expert knowledge or labeled historical data. Since there are often few monitoring data at the beginning of a new incoming operating mode, an adaptive self-transfer learning algorithm based on Gaussian processes is developed to model the monitoring data with uncertainty information, and to capture the cross-correlations between the different normal modes. A two-hierarchical identification criterion based on the predicted posterior intervals is introduced to first identify the change-points in the observations, and second to decide whether it is an anomaly or a transition between normal modes. The proposed framework is tested on a real steam turbine. The results illustrate its high effectiveness.

Time consideration in machine learning models for train comfort prediction using LSTM networks

Safety and passengers’ comfort affect directly the reliability and availability of trains. Therefore, appropriate monitoring of the variables influencing those two concepts is mandatory for good operation and maintenance costs reduction. Moreover, operators and rail manufacturers are evolving to a condition-based maintenance strategy (CBM). For this purpose, machine learning is being used to predict early main component’s failures, avoiding a loss of reliability or availability during commercial operation. From the passengers’ point of view, punctuality and comfortability are two of the most relevant factors when evaluating a railway transport service. Comfort is mainly based on the availability of all services in the train, but it is also related to the accelerations received through the car body to the passengers. More precisely, the lateral car body acceleration (Ayc variable in this paper) is a key variable related to comfort perception and it is therefore important to be monitored and controlled. Rolling, suspension, and damping elements must be kept in perfect conditions during operation to achieve a smooth train movement. In this paper, an optimized model based on Long Short Term Memory networks (LSTM) is designed and trained to predict lateral car body accelerations using only the train accelerometers and with no additional sensoring for this specific experiment. This model will be compared with artificial neural networks (ANN) models to evaluate the relevance of time dependency for this purpose. The optimal time frame to group data is a key characteristic of the neural network training process and is also determined in this paper. The model is trained with data corresponding to the correct dynamic behavior of the train. To test the reliability of this method, a real case of excessive train movement is tested. In these situations, an alert could be sent to the operator or the maintainer for a not-programmed maintenance inspection at the depot (predictive maintenance), avoiding future passenger complaints for the loss of comfort or, in extreme cases, speed limitations due to safety reasons for excessive accelerations. The main contributions of this work are the following:- A LSTM-based neural network, predicting lateral car body accelerations achieving better results than a plain ANN for this dataset: 0.0335 m/s2 MAE and a 12% gain on extreme axles.- An optimization of the time frame to group the measurements in this algorithm that is considering as an input the time variable: 5 s.- A real use case of the model in which monitoring the deviation would have helped to take an early maintenance adjustment on the suspension or damping elements to prevent the loss of comfort.- The p-value used to reduce the uncertainty of the results obtained in the real use case is reaching over 0.95 in the coaches with good dynamic behavior and less than 0.15 in the coaches where a dynamic problem related to comfort loss is detected.

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.

Rotating Machinery Fault Diagnosis Based on Typical Resonance Demodulation Methods: A Review

The increasing integration and complexity of rotating machinery have led to the difficulty of its fault diagnosis. Condition-based maintenance (CBM) strategy is becoming more and more important in the initial fault diagnosis of rotating machinery. Resonance demodulation technology is the one fault diagnosis technology widely used in the machinery industry field. Spectral kurtosis (SK) and frequency band entropy (FBE) are two typical resonance demodulation methods, in which SK has been extensively and deeply studied both theoretically and practically, while the FBE has not attracted much attention from researchers. In this article, two typical resonance demodulation techniques, SK and FBE, and their applications in fault detection of rotating machinery are reviewed and summarized, aiming to provide comprehensive reference resources for the research of the two resonant demodulation methods, FBE and SK. In addition, it is hoped that it will play a certain role in promoting the development of FBE theory. The problems and challenges faced by resonance demodulation technologies, especially SK and FBE, and their applications in mechanical fault diagnosis are analyzed, in order to provide valuable guidance for researchers. Therefore, the prospects of the two methods are also given. Some examples are offered to illustrate their performance. It is reasonable to expect that the further development of SK, and further research and applications of FBE will flourish in the near future.

Condition-Based Maintenance of an Anaerobic Reactor Using Artificial Intelligence

This paper proposes a condition-based maintenance system based on artificial intelligence for an online monitoring system of the support bed expansion in a 30-liter pilot-scale inverse fluidized bed reactor (IFBR). The main scope is to achieve a condition-based maintenance strategy using a single-level sensor for a biofilm inverse fluidizing bed as source for virtual sensors. The implementation of an artificial neural network was performed on an embedded electronic system (Raspberry Pi 4), both working together in real time. The signals estimated by the neural network are compared against the signals measured by the hardware sensors and, in case of detecting a failure in the physical measurement system, the artificial intelligence-based system then uses the signal estimated by the artificial neural network to maintain the correct operation of the IFBR. This system uses an artificial neural network to estimate the COD concentration of the effluent and the biogas production flow of a bioreactor, from the measurement of pH, the COD concentration of the influent, the inflow to the bioreactor and the signal coming from each of the conductivity sensors installed inside the reactor, which provide information about support media expansion in a pilot-scale inverse fluidized bed reactor. In addition, a fuzzy PI controller is presented, which was implemented in a Raspberry Pi electronic card, to regulate the COD concentration in the effluent of the bioreactor used as a case study.

A two-stage Gaussian process regression model for remaining useful prediction of bearings

Bearing is one of the most important supporting components in mechanical equipment and its health status has a significant impact on the overall performance of equipment. The remaining useful life (RUL) prediction of bearings is critical in adopting a condition-based maintenance strategy to ensure reliable equipment operation. To accurately predict the RUL of bearings, this paper proposes a two-stage Gaussian process regression (GPR) model, which combines the flexibility of the Gaussian process and the physical mechanism of the Wiener process. Compared with the conventional GPR model, the proposed model can reasonably adapt to the statistical characteristics of bearings degradation and provide more stable predictions. In addition, the paper proposes a new degradation detection approach based on the Euclidean distance to distinguish the two stages of the bearing service life cycle, which considers the global characteristics of bearing degradation and can accurately detect the beginning point of bearing degradation. The experimental results show that the proposed two-stage GPR model can help to improve the precision and accuracy of degradation path tracking and RUL prediction.

A novel model-based unbalance monitoring and prognostics for rotor-bearing systems

A novel model-based unbalance monitoring and prognostics for rotor-bearing systems is introduced in the paper. An analytical method is first applied for rotor modeling and the calculated first natural frequency is validated by an FEM model. The rotor-bearing model with the identified bearing parameters is next validated with an operational 3-stage turbine-bearing’s machine on the first critical speed. The novelty of the approach is that the unbalance proceeding with optimization schemes is evaluated in two phases. In phase I, the bearing parameters and the initial unbalances are simultaneously evaluated based on the operational data soon after an overhaul. In phase II, the unbalance deterioration with time is identified through every day’s measured vibration at two bearings. A set of operational data over 16 months, provided by a local company, are used to test the approach. The evaluated unbalance deterioration trend is verified by the collaborated company from two consecutive overhauls. Five optimization algorithms are also tested and the results prove the robustness of the derived approach. Finally, the unbalance forecasting capability extrapolating from historical unbalance curve is demonstrated and that can work as prognostics in a condition-based maintenance strategy.

Joint Optimization of Condition-Based Maintenance and Spare Ordering Strategy for a Multistate Competing Failure System

This paper develops a joint strategy of condition-based maintenance and spare ordering for a multistate system which is subject to competing failures due to external shocks and self-degradation. Failures of this system are hidden and can be divided into two types: a one-stage hard failure and a two-stage soft failure. The states of the system are identified by periodic inspections and replacement is executed preventively or correctively in response to the defective or failed state. When the operating time of the system is reached the predetermined threshold τ, the spare is ordered. Furthermore, a threshold level z is introduced to postpone the preventive replacement (PR) when the ordered spare is delivered before the defective state is first detected. Depending on the state of the ordered spare when replacement is required and the fault cause of the system when a corrective replacement (CR) is needed, all possible renewal events are analyzed to establish the joint optimization model. A modified artificial bee colony algorithm and discrete simulation algorithm are adopted to find the optimal solutions, and the correctness of the proposed model is verified by a numerical example. Moreover, the results from the numerical example indicate that the proposed strategy is superior to the comparative model, thus the applicability and effectiveness of the proposed model is illustrated.

A Comparative Study of Preventive Maintenance Thresholds for Deteriorating Systems

Degradation of engineering systems is a phenomenon that affects the reliability and the lifetime of systems. Maintenance of deteriorating systems has been widely studied over the past 50 years. Numerous maintenance models have been proposed in the literature to manage degradation. In this paper, we focus on a condition-based maintenance strategy for those systems suffering a continuous deterioration process. We propose the use of a non-constant maintenance threshold to determine when a preventive action is required. The main goal is to analyse the effect of non-constant threshold in the long-term maintenance cost rate. We demonstrate numerically the convergence of the long run cost rate and some statistical properties of the proposed model. We compare the use of a constant threshold with a non-constant threshold through two different simulated case studies. The simulations provide some statistical results that reveals interesting advantages and drawbacks of using a non-constant threshold.