Condition-based Maintenance Approach Research Articles

Development and validation of a probabilistic multistage algorithm for damage localization in piezo-monitored structures

All structures during operating life can be affected by faults induced by accidental events and operational conditions. Structural health monitoring systems can provide quasi-real-time diagnosis of the structure, thus enabling the condition-based maintenance approach. By means of piezoelectric transducers (PZTs) and ultrasonic guided waves (UGW), the structural integrity can be easily interrogated, even though laborious post-processing techniques are required to correctly interpret sensed data. This work aims to devise a new automatic diagnosis framework based on the propagation of UGW for thin-walled structures fault detection and localisation. Specifically, a fully automated damage identification algorithm was developed through a numerical dataset obtained by finite element simulations, and then validated experimentally. The case of study consisted of a square-shaped aluminium plate equipped with a five PZTs network. Five different damage positions and three different damage sizes were considered. The originality of the proposed algorithm lies in the data processing methodology as well as in its capability to detect damages located inside and outside the sensors network, even close to the panel edges. Algorithm provides, in less than 15 s, indications on the possible damage location and related probability position with a reduced dispersion with respect to other algorithms proposed in literature. A clear image is created displaying the damage position map. The visualisation of the damage position map on the surface of the monitored part allows successful damage imaging and would enable operators to address more efficiently the inspection procedures only in the highlighted areas, reducing maintenance and repair expenses.

Data Acquisition for Condition Monitoring in Tactical Vehicles: On-Board Computer Development

This paper outlines the development of an onboard computer prototype for data registration, storage, transformation, and analysis. The system is intended for health and use monitoring systems in military tactical vehicles according to the North Atlantic Treaty Organization Standard Agreement for designing vehicle systems using an open architecture. The processor includes a data processing pipeline with three main modules. The first module captures the data received from sensor sources and vehicle network buses, performs a data fusion, and saves the data in a local database or sends them to a remote system for further analysis and fleet management. The second module provides filtering, translation, and interpretation for fault detection; this module will be completed in the future with a condition analysis module. The third module is a communication module for web serving data and data distribution systems according to the standards for interoperability. This development will allow us to analyze the driving performance for efficiency, which helps us to know the vehicle’s condition; the development will also help us deliver information for better tactical decisions in mission systems. This development has been implemented using open software, allowing us to measure the amount of data registered and filter only the relevant data for mission systems, which avoids communication bottlenecks. The on-board pre-analysis will help to conduct condition-based maintenance approaches and fault forecasting using the on-board uploaded fault models, which are trained off-board using the collected data.

LSTM based artificial intelligence predictive maintenance technique for availability rate and OEE improvement in a TPM implementing plant through Industry 4.0 transformation

PurposeThe artificial intelligence (AI) based total productive maintenance (TPM) condition based maintenance (CBM) approach through Industry 4.0 transformation can well predict the breakdown in advance to eliminate breakdown.Design/methodology/approachMeeting the customer requirement as per the delivery schedule with the existing resources are always a big challenge in industries. Any catastrophic breakdown in the equipment leads to increase in production loss, damage to machines, repair cost, time and affects delivery. If these breakdowns are predicted in advance, the breakdown can be addressed before its occurrence and the demand supply chain can be met. TPM is one of the essential operational excellence tool used in industries to utilize the existing resources of a plant in a optimal way. The conventional time based maintenance (TBM) and CBM approach of TPM in Industry 3.0 is time consuming and not accurate enough to achieve zero down time.FindingsThe proposed AI and IIoT based TPM is achieved in a digitalized data oriented platform to monitor and control the health status of the machine which may reduce the catastrophic breakdown by 95% and also improves the quality rate and machine performance rate. Based on the identified key signature parameters related to major breakdown are measured using the sensors, digitalised by programmable logic controller (PLC) and monitored by supervisory control and data acquisition (SCADA) and predicted in server or cloud.Originality/valueLong short term memory based deep learning network was developed as a regression forecasting model to predict the remaining useful life RUL of the part or assembly and based on the predictions, corrective action has been implemented before the occurrence of breakdown. The reliability and consistency of the proposed approach are validated and horizontally deployed in similar machines to achieve zero downtime.

IoT Based Condition Monitoring for Railway Track Fault Detection in Smart Cities

The railways have the busiest and biggest networks in the world and play a key role in driving the economic growth of any country. The railways still follow the traditional method of manual inspection which is very time-consuming and not economical. The proposed work demonstrates maintenance i.e. condition monitoring based fault detection. In this method, the system will measure accelerations at the train bogie and can find railway track longitudinal profile, and rail condition from the response provided by inertial sensors mounted on the in-service train. The vertical and lateral accelerations of railway bogies are used to evaluate faults in railway tracks. A tachometer system and a map-matching algorithm are used to pinpoint the location of faults on tracks. This system will communicate these vibration messages wirelessly to a cloud service, which processes the data, and using knowledge of sensor location and vibration history the determination of the track condition is performed on a regular basis. This data is then conveyed to infrastructure managers in the form of alerts, thereby facilitating a condition-based maintenance approach equipped with IoT. The system can easily indicate the weak locations in the track that can be further analyzed in detail by the diagnostician. Thus, the system aids in tracking maintenance strategies and will reduce downtime. This condition-based maintenance system provides smart detection of faults as compared with traditional methods.

Predictive Modeling of Local Film-Cooling Flow on a Turbine Rotor Blade

Abstract In the turbine section of a modern gas turbine engine, components exposed to the main gas path flow rely on cooling air to maintain hardware durability targets. Therefore, monitoring turbine cooling flow is essential to the diagnostic and prognostic efficacy of a condition-based operation and maintenance (CBOM) approach. This study supports CBOM goals by leveraging supervised machine learning to estimate relative changes to local film-cooling flowrate using surface temperature measured on the pressure side of a rotating turbine blade operating at engine-relevant aerothermal conditions. Throughout the lifetime of a film-cooled turbine component, characteristics of the film-cooling flow—such as film trajectory and cooling effectiveness—vary as degradation-driven geometry distortions occur, which ultimately affects the relationship between the model input and the model output—film-cooling flowrate predictions. The present study addresses this complication by testing a data-driven model on multiple turbine blades of the same nominal design, but with each blade exhibiting different localized film-cooling flow characteristics. By testing the model in this manner, strategies for mitigating the detrimental effects of film-cooling flow characteristic variations on model performance were investigated, and the corresponding flowrate prediction accuracy was quantified.

Sustainability-oriented maintenance management of highway bridge networks based on Q-learning

Current bridge management systems provide maintenance strategies that balance costs and structural safety of bridge networks. However, they are cost-oriented rather than sustainability-oriented. Moreover, the existing literature lacks research on reducing the environmental impacts of a bridge network during operation and maintenance periods. This paper develops a condition-based maintenance approach for highway bridges, aiming to minimize the total carbon emissions of bridge networks subject to maintenance budget constraints. A two-dimensional Markov chain model is applied to predict the deterioration processes of bridges, and a Q-learning algorithm is proposed to determine the maintenance strategy for a single bridge. Then, integer programming optimizes the budget allocation process for a bridge network. The proposed method is demonstrated using the concrete bridges in New York state. Sensitivity analyses indicate the impacts of budget levels on maintenance plans and total carbon emissions. 10% drop in budget level results in 30 additional bridge repairs delayed as well as 39 tonne extra annual carbon emissions. The proposed optimization framework would contribute to intelligent infrastructure asset management and sustainable society.

Online-monitoring of motor operated valves

Abstract Traditionally, evaluating the running behaviour of motor operated valves (MOV) is based upon a non-continuous and demand oriented method. Gundremmingen Nuclear Power Plants has improved this strategy over the last 3 years. At MOVs within the safety system, diagnostics modules were installed for an online monitoring of active power during every run on demand. In the near future, functionality will be further enhanced in order to increase the particular knowledge, as a basis for both a safety oriented, but optimised, condition-based preventive maintenance approach and a plant life management strategy. Furthermore, during spring outage of unit B in 2002, a benchmark was implemented at four MOVs, which includes both comparison between two different kinds of power monitoring as well as active control of one of the systems.

A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance

Offshore wind farms are a rapidly developing source of clean, low-carbon energy and as they continue to grow in scale and capacity, so does the requirement for their efficient and optimised operation and maintenance. Historically, approaches to maintenance have been purely reactive. However, there is a movement in offshore wind, and wider industry in general, towards more proactive, condition-based maintenance approaches which rely on operational data-driven decision making. This paper reviews the current efforts in proactive maintenance strategies, both predictive and prescriptive, of which the latter is an evolution of the former. Both use operational data to determine whether a turbine component will fail in order to provide sufficient warning to carry out necessary maintenance. Prescriptive strategies also provide optimised maintenance actions, incorporating predictions into a wider maintenance plan to address predicted failure modes. Beginning with a summary of common techniques used across both strategies, this review moves on to discuss their respective applications in offshore wind operation and maintenance. This review concludes with suggested areas for future work, underlining the need for models which can be simply incorporated by site operators and integrate live data whilst handling uncertainties. A need for further focus on medium-term planning strategies is also highlighted along with consideration of the question of how to quantify the impact of a proactive maintenance strategy.

Total Unwrapped Phase-Based Diagnosis of Wall Thinning in Nuclear Power Plants Secondary Piping Structures

Inspection-based techniques are being widely used for aging management of passive structures in nuclear power plants (NPPs). This paper presents a condition-based maintenance approach to detect corrosion in NPPs secondary piping structures using distributional features extracted from the total unwrapped phase signal of accelerometers. To demonstrate a condition-based maintenance approach, a scaled-down experimental testbed and data-driven methodology are developed and validated for detecting surrogate corrosion processes in a piping structure. The surrogate corrosion process is emulated by removing different levels of mass from the pipe-bend section and measuring the vibration of the pipe bend using five tri-axial accelerometers. The data collected are processed using the Hilbert-Huang transform. Distributional features of the total unwrapped phase from each accelerometer are used to develop a binary classifier to predict a degraded pipe against baseline pipes (healthy state). The binary classification model is extended to a multiclass classification to predict the level of mass removal. Binary and multiclass classification are performed using traditional machine learning algorithms such as logistic regression, neural networks, random forest, and support vector machine. All the algorithms achieved at least 99% prediction accuracy in each direction of the tri-axial accelerometer and significantly outperformed intrinsic mode function-based prediction methods. Performance of classification algorithms were evaluated using data for different number of the tri-axial accelerometers, including a single sensor. The overall prediction accuracy for all these cases was over 99%. Also, the models were interpreted using Shapley additive explanation values to understand the contribution of each input feature in diagnosing the percentage of mass removal. Finally, the performance of a transfer learning model is demonstrated to adopt a machine learning model on to an another elbow, and the prediction performance over 99% is achieved with partial retraining.

Optimal Service Points (OSP) for PHM Enabled Condition Based Maintenance for Oil and Gas Applications

In recent times, the oil and Gas industry has faced many challenges resulting from a tightening climate policy environment on oil and gas exploration, as well as the increasing risk of oversupply due to new discoveries globally. This has given stakeholders in the industry an incentive to integrate new technologies to optimize the operational efficiency of their assets, leading to the optimal recovery of hydrocarbons especially in marginal fields. Various Original Equipment Manufacturers (OEM) now provide different service offerings using data driven methods to provide condition monitoring of assets for oil and gas operators. However, a significant part of the value proposition by OEMs in their service delivery focuses on value generated at the component level with a reduction in asset downtime. This limits the broad economic benefits that a condition-based approach can provide, at the enterprise level. Therefore, the purpose of this paper is to develop a cost benefit analysis framework for assessing the implementation of condition and performance monitoring of oil and gas assets used in surface applications. The framework utilizes a combined technical-economic approach to determine a minimum predictive requirement for the implementation of condition-based principles to maintenance of assets in a hydrocarbon project from first oil to abandonment. This financial analysis framework uses a condition monitoring approach based on prognostics as well as a regression approach for fault detection and system performance. The paper will present a case study to evaluate the costs and benefits associated with implementing a condition-based maintenance approach for a set of valves in a Christmas tree subsystem, as part of a typical onshore production system. The framework illustrated using the case study compares a constant failure rate Time-Based approach to the PHM enabled condition-based maintenance. The results demonstrate that a prognostic enabled system can provide commercial benefits at the component level for a Condition Based Maintenance strategy but not necessarily at the enterprise level for an oil and gas project. The cumulative reduction in downtime at the component level over a project lifecycle offsetting the present value of the total cost of integrating a PHM enabled system into the overall maintenance strategy creates the ideal situation for commercial viability. However, the commercial viability of the PHM integration would depend on the accuracy of predicting failure events and monitoring asset degradation by the PHM enabled system which ultimately defines the performance of the condition-based maintenance approach. The accuracy level of asset failure therefore provides OEMs with a benchmark for executing their condition-based maintenance services with a minimum performance threshold. Secondly, an enterprise level financial viability, as well as OEM profitability in the implementation of a condition-based maintenance approach, requires an Optimal Service Point (OSP) which is a function of the minimum predictive requirement of the PHM system. The utility that the OSP provides is that, it gives the minimum value of the framework’s decision criteria that an operator can use a basis for incorporating a condition-based approach in its maintenance strategy. It also provides the maximum Annual Service Fee (ASF) derived from the cumulative OEM NPV needed for structuring and pricing servitization agreements with operators. This OSP cost benefit analysis approach ultimately provides OEMs and operators with a practical guide in the provision as well as the adoption of condition-based maintenance strategies respectively. It balances the risk of PHM integration by operators with a minimum PHM system performance threshold required for commercial viability for project lifecycle.

Optimization of condition-based maintenance strategy prediction for aging automotive industrial equipment using FMEA

Maintenance plays a highly important role in achieving production targets and system performance. Electromechanical equipment and facility infrastructure within motor manufacturing industries are expected to perform at optimal efficiency during the operational phase of production. A major problem in the automotive production plan from motor industry statistics is associated with unexpected downtime, which is largely linked to aging equipment. During production downtime, much time is lost to fault finding, repairs, and replacement of faulty components within production lines. This transforms into low throughput in production, and performance gradually declines during the operational life cycle of the equipment. This paper presents an approach taken to prevent such instances in the automotive manufacturing industry, which considers an optimized condition-based maintenance approach to predict the condition of each component and assembly line using Failure-Mode-and-Effect-Analysis (FMEA). The condition-based performance level prediction is designed to help in formulating maintenance schedules and strategies that eliminate unplanned downtimes.

A deep supervised learning approach for condition-based maintenance of naval propulsion systems

In the last years, predictive maintenance has gained a central position in condition-based maintenance tasks planning. Machine learning approaches have been very successful in simplifying the construction of prognostic models for health assessment based on available historical labeled data issued from similar systems or specific physical models. However, if the collected samples suffer from lack of labels (small labeled dataset or not enough samples), the process of generalization of the learning model on the dataset as well as on the newly arrived samples (application) can be very difficult. In an attempt to overcome such drawbacks, a new deep supervised learning approach is introduced in this paper. The proposed approach aims at extracting and learning important patterns even from a small amount of data in order to produce more general health estimator. The algorithm is trained online based on local receptive field theories of extreme learning machines using data issued from a propulsion system simulator. Compared to extreme learning machine variants, the new algorithm shows a higher level of accuracy in terms of approximation and generalization under several training paradigms.

Impact of Prognostic Uncertainty in System Health Monitoring

Across many industries, systems are exceeding their intended design lives, whether they are ships, bridges or military aircraft. As a result failure rates can increase and unanticipated wear or failure conditions can arise. Health monitoring research and application has the potential to more safely lengthen the service life of a range of systems through utilization of sensor data and knowledge of failure mechanisms to predict component life remaining. A further benefit of health monitoring when combined across an entire platform is system health management. System health management is an enabler of condition based maintenance, which allows repair or replacement based on material condition, not a set time. Replacement of components based on condition can enable cost savings through fewer parts being used and the associated maintenance costs. The goal of this research is to show the management of system health can provide savings in maintenance and logistics cost while increasing vehicle availability through the approach of condition based maintenance.This work examines the impact of prediction accuracy uncertainty in remaining useful life prognostics for a squadron of 12 aircraft. The uncertainty in this research is introduced in the system through an uncertainty factor applied to the useful life prediction. An ARENA discrete event simulation is utilized to explore the effect of prediction error on availability, reliability, and maintenance and logistics processes. Aircraft are processed through preflight, flight, and post-flight operations, as well as maintenance and logistics activities. A baseline case with traditional time driven maintenance is performed for comparison to the condition based maintenance approach of this research.This research does not consider cost or decision making processes, instead focusing on utilization parameters of both aircraft and manpower. The occurrence and impact of false alarms on system performance is examined. The results show the potential availability, reliability, and maintenance benefits of a health monitoring system and explore the diagnostic uncertainty.

A feature fusion-based prognostics approach for rolling element bearings

The emergence of prognostics and health management as a condition-based maintenance approach has greatly improved productivity, maintainability, and most essentially, reliability of systems. Invariably, a rolling-element bearing (REB) is the heart of rotating components; however, its failure can have daunting effects ranging from costly unexpected breakdown to catastrophic life-threatening situations. Consequently, the need for accurate condition monitoring and prognostics of REBs cannot be overemphasized. In view of achieving a more comprehensive condition assessment for prognostics of REBs, this study proposes a kernel principal component analysis (KPCA) feature fusion technique for degradation assessment and a deep learning model for prognostics. The deep learning method-deep long short-term memory (DLSTM) has shown an evident comparative advantage over the basic LSTM model and standard recurrent neural networks for time-series forecasting. Subsequently, the proposed prognostics model-KPCA-DLSTM performance was validated with a run-to-failure experiment on REBs and evaluated for accuracy against other prognostics methods reported in other works of literature using standard performance metrics. The proposed method was also used for REB remaining useful life (RUL) prediction and the results show that the KPCA-DLSTM does not only reflect a more monotonic bearing degradation trend but also yields better prognostics results.

An Open Source Framework Approach to Support Condition Monitoring and Maintenance

This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture–condition-based maintenance (OSA–CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability.

Technical data-driven tool condition monitoring challenges for CNC milling: a review

CNC milling is a highly complex machining process highly valued in various industries, including the automotive and aerospace industries. With the increasing competition, manufacturers are aiming to keep maintenance costs low while ensuring high levels of manufacturing equipment reliability. It is also highly important for them to maximize the service life of each cutting tool by avoiding premature replacements while minimizing the risks of scrap due to tool breakage. This calls for the need for a condition-based maintenance approach and more powerful, flexible and robust tool condition monitoring (TCM) techniques with minimal reliance on subjective diagnosis based on the expert knowledge. This paper discusses the technical aspects of recent developments in state-of-the-art TCM techniques and current challenges which limit the viability of TCM in real-life industrial applications. The technical challenges in modern TCM were split into two major groups of problems: (1) challenges in data processing and (2) issues regarding tool wear model performance. Current methodologies to overcome issues in each of the sections in this paper are discussed and, where possible, compared to highlight their respective advantages and disadvantages. Finally, this paper concludes with a discussion on possible trends in TCM development and interesting avenues for future research.

A one-class SVM based approach for condition-based maintenance of a naval propulsion plant with limited labeled data

Maintenance of the main propulsion system is an important part of ship operation, whose costs account for the most of the costs of ship machinery maintenance. Related literature has shown that condition-based maintenance can minimize maintenance costs and machinery downtime. In this respect, we propose a one-class support vector machine based approach for machinery decay status assessment which helps to achieve condition-based maintenance. The novelty of our work lies in the using of decision values returned by the trained models to estimate the decay degree and the main decay direction. In particular, only the normal data and a small amount of labeled decayed data is necessary in model training process which greatly reduces the requirement for labeled data. Consequently, it can be used as a decision support tool for condition-based maintenance not only on this plant, but also on many other machinery.

Stochastic Filtering Approach for Condition-Based Maintenance Considering Sensor Degradation

This paper proposes a condition-based maintenance (CBM) policy for a deteriorating system whose state is monitored by a degraded sensor. In the literature of CBM, it is commonly assumed that inspection of system state is perfect or subject to measurement error. The health condition of the sensor, which is dedicated to inspect the system state, is completely ignored during system operation. However, due to the varying operation environment and aging effect, the sensor itself will suffer a degradation process and its performance deteriorates with time. In the presence of sensor degradation, the Kalman filter is employed in this paper to progressively estimate the system and the sensor state. Since the estimation of system state is subject to uncertainty, maintenance solely based on the estimated state will lead to a suboptimal solution. Instead, predictive reliability is used as a criterion for maintenance decision-making, which is able to incorporate the effect of estimation uncertainty. Preventive replacement is implemented when the estimated system reliability at inspection hits a specific threshold, which is obtained by minimizing the long-run maintenance cost rate. An example of wastewater treatment plant is used to illustrate the effectiveness of the proposed maintenance policy. It can be concluded through our research that: 1) disregarding the sensor degradation while it exists will significantly increase the maintenance cost and 2) the negative impact of sensor degradation can be diminished via proper inspection and filtering methods. Note to Practitioners —This paper was motivated by the observation of sensor degradation in wastewater treatment plants but the developed approach also applies to other systems such as manufacturing systems, chemical plants, and pharmaceutical factories, where sensors are dedicated to a long-time operation in a harsh environment. This paper investigates the impact of sensor degradation on CBM and suggests that the effect of sensor degradation should be carefully addressed while making maintenance decisions. Otherwise, it will lead to a suboptimal maintenance decision and increase the operating cost. An optimal maintenance decision, which contains the optimal inspection interval and reliability threshold, is achieved via minimizing the long-run cost rate. In the presence of measurement noise and intrinsic uncertainty from degradation, a stochastic filtering approach is employed to estimate the system and sensor state. Based on the estimated states and the calculated reliability, a dynamic maintenance decision is obtained at each inspection. This paper can be further extended considering non-Gaussian noise and alternative degradation processes.

A rolling horizon approach for production planning and condition-based maintenance under uncertain demand

In reality, the forecast of uncertainties often becomes more accurate with the approaching of the forecasted period. This article proposes a rolling horizon approach to dynamically determine the production plan and the maintenance plan for a degradation system under uncertain environment. In each rolling horizon, demand forecasts are updated with new information from customers, and the degradation level of system is confirmed by inspection. By taking advantage of the updated uncertainties, at each decision point, the maintenance plan is determined by an advance-postpone balancing approach and the production plan is optimized by a heuristic algorithm in a two-stage stochastic model. Numerical results validate that the rolling horizon approach has great superiority over traditional stochastic programming approach in terms of real total cost and service level.

Maintenance Decision-Making Support for Textile Machines: A Knowledge-Based Approach Using Fuzzy Logic and Vibration Monitoring

A condition-based maintenance approach may be used for planning the maintenance activities of textile machines with a satisfactory performance by developing maintenance decision-making support based on fuzzy logic and vibration monitoring. Since textile machines are systems with moving parts operating at relatively high-speed, vibration monitoring was used to indicate their failure development. At the same time, the characterization of the degradation phenomenon of textile machines involves some degree of uncertainty and vagueness. Within this context, a knowledge-based approach that employed fuzzy logic and vibration monitoring was developed. Deterioration symptoms do announce future failures of industrial machines, therefore building a maintenance decision-making support for scheduling maintenance actions of textile machines based on the estimation of their condition becomes a resourceful way to prevent their further deterioration.