Application Of Condition-based Maintenance Research Articles

A MapReduce-Based Ensemble Learning Method with Multiple Classifier Types and Diversity for Condition-based Maintenance with Concept Drifts

Condition-based maintenance (CBM) is to collect a huge amount of production data stream continuously from sensors or IoT devices attached to machines to forecast the time when to maintain machines or replace components. However, as conditions of machines change dynamically with time owing to machine aging or malfunction, the concept of capturing the foresting pattern from the data stream could drift unpredictably so that it is hard to find a robust forecasting method with high precision. Therefore, this work proposes an ensemble learning method with multiple classifier types and diversity for CBM in manufacturing industries. Aside from manipulating data diversity from previous works, the proposed method includes the features of multiple classifier types, dynamic weight adjusting, and data-based adaption to concept drifts for offline learning models, to promote the precision of the forecasting model and precisely detect and adapt to concept drifts. With these features, the proposed method requires powerful computing resources to efficiently respond to practical CBM applications in manufacturing industries. Therefore, furthermore, the implementation of the proposed method based on the MapReduce framework is proposed to increase computational efficiency. Simulation shows that the proposed method can detect and adapt to all concept drifts with a high precision rate.

Knowledge Embedded Lightweight Vision Transformer for Machine Condition Monitoring

Machine condition monitoring methods are a powerful tool for the physical condition evaluation of high-end equipment. As one of the main manufacturing applications of machine condition monitoring, tool performance evaluation (TPE) suffers the limitation of model generalization ability across multiple operating conditions. Subsequently, designing a method that can meet the diversified industrial scenarios’ requirements to confirm the cutters’ machinability is of great practical significance. However, recent TPE studies indicate that the on-machine measurement processes are predominantly affected by the interference of ambient noise and signal aliasing of multi-source sensors. Recent advances in knowledge-embedding and artificial intelligence bring new vitality to the development of industrial condition-based maintenance applications. Considering the above-mentioned analysis, this paper proposes a new knowledge-embedded intelligent TPE method, where the prior knowledge of machining parameters is fused with features extracted from multiple sensor information. The proposed method gives an effective solution for TPE tasks under multiple working conditions.

Application of condition-based maintenance for electrical generators based on statistical control charts

Condition-based maintenance involves activities that are conducted based on the equipment's performance. Continuous monitoring of equipment will ensure that it will be maintained according to a relevant activity plan. This paper proposes a maintenance framework to analyze the application of statistical control charts for condition-based maintenance of electrical generators. The proposed framework consists of four components that collaboratively determine a performance threshold for a given piece of electrical equipment. Based on the slow progression and dynamics of mechanical failures, Long Short-term Memory (LSTM) and Useful Remaining Life (URL) models were used to assist in the maintenance decision-making process. The analysis is based on detecting the dynamics of the process parameters, including vibration, noise, and temperature, based on relevant control charts. With the help of experimental methodology, failures in the performance modes and defined modes are measured. Then empirical analysis reveals how control charts respond to failure detection. The results show that X-bar consistently demonstrates failure detection capability, while R charts sometimes fail when data deviates from normality.Moreover, heat monitoring surpassed vibration and noise in failure detection, where temperature control charts successfully identified failure. The overall results support the significant role of statistical charts in decision-making regarding condition-based maintenance for electrical equipment like generators.•Application of statistical control charts for condition-based maintenance of electrical generators.•Detecting dynamics of the process parameters, including vibration, noise, and temperature, based on relevant control charts.•Long Short-term Memory (LSTM) and Useful Remaining Life (URL) models were used to assist in the maintenance decision-making process

Digital twins in condition-based maintenance apps: A case study for train axle bearings

Digital Twins (DTs) are gaining popularity in the context of the fourth industrial revolution to replicate physical equipment and systems in the digital world. DTs promise increased productivity and sustainable performance by integrating data, models, and decision-support systems. However, before realizing the potential benefits of DTs for maintenance management, several challenges need to be addressed, including a lack of conceptual basis, functional description, and established requirements. Hence, the paper presents, in a practical manner, how to cover this gap in digital configurations for maintenance management, designed to benefit of DTs. The scope of the paper includes the design and implementation of an innovative condition-based maintenance application (CBM App) based on a DT of train axle bearings, and uses a generic framework for digital maintenance management for the functional description of the DT within the CBM App. The paper provides details of the models and algorithms used to build the DT and ensures that recommended features are fulfilled. To test the DT's effectiveness and robustness, the design and framework are implemented in real CBM applications of TALGO, a high-speed train manufacturer. These tools are deemed helpful for easing DT implementation within the CBM App and can be replicated in other operational contexts.

Comparison of population-based algorithms for parameter identification for induction machine modeling

PurposeMonitoring and diagnosis of fault cases for squirrel cage induction motors can be implemented using the multiple coupled circuit model. However, the identification of the associated model parameters for a specific machine is problematic. Up to now, the main options are measurement and test procedures or the use of finite element method analyses. However, these approaches are very costly and not suitable for use in an industrial application. The purpose of this paper is a practical parameter identification based on optimization methods and a comparison of different algorithms for this task.Design/methodology/approachPopulation-based metaheuristics are used to determine the parameters for the multiple coupled circuit model. For this purpose, a search space for the required parameters is defined without an elaborate analytical approach. Subsequently, a genetic algorithm, the differential evolution algorithm and particle swarm optimization are tested and compared. The algorithms use the weighted mean squared error (MSE) between the real measured data of stator currents as well as speed and the simulation results of the model as a fitness function.FindingsThe results of the parameter identification show that the applied methodology generally works and all three optimization algorithms fulfill the task. The differential evolution algorithm performs best, with a weighted MSE of 2.62, the lowest error after 1,000 simulations. In addition, this algorithm achieves the lowest overall error of all algorithms after only 740 simulations. The determined parameters do not completely match the parameters of the real machine, but still result in a very good reproduction of the dynamic behavior of the induction motor with squirrel cage.Originality/valueThe value of the presented method lies in the application of condition-based maintenance of electric drives in the industry, which is performed based on the multiple coupled circuit model. With a parameterized model, various healthy as well as faulty states can be calculated and thus, in the future, monitoring and diagnosis of faults of the respective motor can be performed. Essential for this, however, are the parameters adapted to the respective machine. With the described method, an automated parameter identification can be realized without great effort as a basis for an intelligent and condition-oriented maintenance.

Combined application of condition-based maintenance and reliability centred maintenance using PFMEA and lean concepts - a case study

Combined application of condition-based maintenance and reliability centred maintenance using PFMEA and lean concepts - a case study

Machining Tool and Bearing Failure Analysis Using the Orthogonal Hilbert-Huang Transform on Vibration and Motor Current Datasets


 
 
 Early fault detection is the primary goal of condition-based maintenance (CBM); to identify an impending fault before failure occurs and provide the necessary maintenance in a timely manner. Machining tools, such as drill bits and milling inserts, require a significant amount of maintenance during industrial machining operations to ensure that workpiece tolerances are met. Faulty tools tend to machine outside expected tolerances and run the risk of causing permanent damage to the workpiece, resulting in added production costs and unwanted delays. Similarly, ball bearing maintenance is crucial for keeping a machine in acceptable working condition, and has been the primary focus of many CBM studies due to nonlinear bearing degradation.
 The orthogonal Hilbert-Huang transform (OHHT) is a recent improvement to the relatively new Hilbert-Huang transform (HHT) that is significantly more computationally efficient— compared to other improved HHT algorithms—and reduces the amount of energy leakage caused by non-orthogonal intrinsic mode functions. The HHT and its derivatives are being explored in CBM applications due to its adaptive nature in analyzing nonlinear and non-stationary phenomena, which is characteristic of machining induced vibrations. It has also shown promise in diagnosing machine tool wear from motor current signals compared to commonly used techniques such as wavelet analysis. This paper showcases the OHHT’s potential as a useful diagnostics tool for analyzing machine induced vibration and motor current signals.
 
 

The Need for Multi-Sensor Data Fusion in Structural Health Monitoring of Composite Aircraft Structures

With the increased use of composites in aircraft, many new successful contributions to the advancement of the structural health monitoring (SHM) field for composite aerospace structures have been achieved. Yet its application is still not often seen in operational conditions in the aircraft industry, mostly due to a gap between research focus and application, which constraints the shift towards improved aircraft maintenance strategies such as condition-based maintenance (CBM). In this work, we identify and highlight two key facets involved in the maturing of the SHM field for composite aircraft structures: (1) the aircraft maintenance engineer who requires a holistic damage assessment for the aircraft’s structural health management, and (2) the upscaling of the SHM application to realistic composite aircraft structures under in-service conditions. Multi-sensor data fusion concepts can aid in addressing these aspects and we formulate its benefits, opportunities, and challenges. Additionally, for demonstration purposes, we show a conceptual design study for a fusion-based SHM system for multi-level damage monitoring of a representative composite aircraft wing structure. In this manner, we present how multi-sensor data fusion concepts can be of benefit to the community in advancing the field of SHM for composite aircraft structures towards an operational CBM application in the aircraft industry.

Data Preparation in Machine Learning for Condition-based Maintenance

Using Machine Learning (ML) prediction to achieve a successful, cost-effective, Condition-Based Maintenance (CBM) strategy has become very attractive in the context of Industry 4.0. In other fields, it is well known that in order to benefit from the prediction capability of ML algorithms, the data preparation phase must be well conducted. Thus, the objective of this paper is to investigate the effect of data preparation on the ML prediction accuracy of Gas Turbines (GTs) performance decay. First a data cleaning technique for robust Linear Regression imputation is proposed based on the Mixed Integer Linear Programming. Then, experiments are conducted to compare the effect of commonly used data cleaning, normalization and reduction techniques on the ML prediction accuracy. Results revealed that the best prediction accuracy of GTs decay, found with the k-Nearest Neighbors ML algorithm, considerately deteriorate when changing the data preparation steps and/or techniques. This study has shown that, for effective CBM application in industry, there is a need to develop a systematic methodology for design and selection of adequate data preparation steps and techniques with the proposed ML algorithms.

Condition-based maintenance strategy optimization of meta-action unit considering imperfect preventive maintenance based on Wiener process

With the popularization and application of technologies such as monitoring and sensing, the application of condition-based maintenance has become increasingly widespread. However, most traditional condition-based maintenance strategies focus on a component or system, which is too large for fault diagnosis and location. Moreover, imperfect maintenance is seldom considered and most of them only optimize the single variable in condition-based maintenance decision. In this paper, the meta-action unit is taken as the research object, and the Wiener process is used to describe the performance degradation of the meta-action unit. Considering that the maintenance quality deteriorates with the increase of the number of maintenance, and has randomness, the beta distribution is used to establish an imperfect preventive maintenance quality model. A new maintenance optimization strategy is proposed, with the long-term maintenance cost rate as the goal, to obtain the best preventive maintenance threshold, inspection cycle and the optimal number of maintenance. Finally, a Monte Carlo simulation was used to solve numerically and analyze the influence of each parameter on the optimal decision. It can provide a reference for the decision-making when developing maintenance policies.

A Condition-Based Maintenance Model Including Resource Constraints on the Number of Inspections

This article presents a stochastic dynamic programming model for a condition-based maintenance application. The approach seeks to determine the most opportune moment to inspect and execute preventive maintenance over each component of a nonredundant system, where the number of inspections to be performed simultaneously during each period is limited due to resource constraints. The model minimizes the total maintenance cost per unit of time, considering failure, maintenance, and inspection costs. Unlike most related literature, the model proposed herein allows nonperiodic inspections; it does not require to predefine a maintenance threshold and does not necessarily connect inspections to maintenance actions. Also, the criticality of each component is not static through time, or defined beforehand, but dynamically determined according to the available resources and the risk of failure. A numerical example illustrates the performance of the proposed model in comparison to three traditional maintenance models, namely corrective maintenance, age-based maintenance, and condition-based maintenance with periodic inspections. Results suggest that the proposed model yields the best solution among the studied policies and is more efficient, with a significant reduction of 90% in inspection resources.

Modelling and application of condition-based maintenance for a two-component system with stochastic and economic dependencies

This paper develops a model of a condition-based maintenance policy for a two-component system with both stochastic and economic dependencies. The stochastic dependency is such that the degradation rate of each component depends not only on its own state (degradation level) but also on the state of the other component. The economic dependency is such that combining multiple maintenance activities has lower cost than performing maintenance on components separately. To select a component or components to be preventively maintained, adaptive preventive maintenance and opportunistic maintenance rules are proposed. A cost model is developed to find the optimal values of decision variables. A case study of a gearbox system demonstrates the utility of the proposed model.

Condition-based Maintenance (CBM) 적용 장비 선정을 위한 기본설계 단계의 LNG-FPSO 가용도 추산

Condition-based Maintenance (CBM) 적용 장비 선정을 위한 기본설계 단계의 LNG-FPSO 가용도 추산

Fleet-level selective maintenance problem under a phased mission scheme with short breaks: A heuristic sequential game approach

Abstract Selective maintenance is the most widely used strategy for identifying and performing the maintenance actions necessary for fleet mission success. A fleet of equipment is usually required to perform phased missions with short scheduled breaks. In this case, a selective maintenance model should be extended for frequency-based maintenance optimization. We research the problem considering the application of condition-based maintenance (CBM). The problem is formulated with the objective of reducing the repair frequency and cost. The constraint is the reliability of the phased mission, and the variables are the remaining useful lifetimes (RUL) of all the key subsystems. The equipment can be classified into three echelons based on the health status before each wave of a mission, and a heuristic game framework with state backtracking is proposed for the three echelons to solve the problem. The flowchart and heuristic rules of the game framework are given, and the game algorithms for the second and third echelons are presented. The second echelon algorithm aims to select the dispatched equipment for the current wave and minimize maintenance, and the third echelon algorithm aims to ensure that sufficient equipment is available for the next wave by performing necessary maintenance. Finally, we present two types of strategy adjustment methods with state backtracking to turn infeasible solutions into feasible solutions and to optimize feasible solutions. To verify the capacity of the proposed method, a case involving a fleet of 12 aircraft is analyzed for a three-mission scheme, and the aircraft repair times and costs are reduced by the method.

Condition monitoring based on modified CUSUM and EWMA control charts

PurposeThe application of condition-based maintenance on selected equipment can allow online monitoring using fixed, half-fixed or portable sensors. The collected data not always allow a straightforward interpretation and many false alarms can happen. The paper aims to discuss these issues.Design/methodology/approachStatistical techniques can be used to perform early failure detection. With the application of Cumulative Sum (CUSUM) Modified Charts and the Exponentially Weighted Moving Average (EWMA) Charts, special causes of variation can be detected online and during the equipment functioning. Before applying these methods, it is important to check data for independence. When the independence condition is not verified, data should be modeled with an ARIMA (p, d, q) model. Parameters estimation is obtained using the Shewhart Traditional Charts.FindingsWith data monitoring and statistical methods, it is possible to detect any system or equipment failure trend, so that we can act at the right time to avoid catastrophic failures.Originality/valueIn this work, an electro pump condition is monitored. Through this process, an anomaly and four stages of aggravation are forced, and the CUSUM and EWMA modified control charts are applied to test an online equipment monitoring. When the detection occurs, the methodology will have rules to define the degree of intervention.

A MapReduce-Based Ensemble Learning Method with Multiple Classifier Types and Diversity for Condition-Based Maintenance with Concept Drifts

Condition-based maintenance in Industry 4.0 collects a huge amount of production datastreams continuously from the Internet of Things attached to machines to forecast the time when to maintain machines or replace components. However, as conditions of machines change dynamically with time owing to machine aging, malfunction, or replacement, the concept of capturing the forecasting pattern from the datastream could drift unpredictably, so it is hard to find a robust forecasting method with high precision. Therefore, this work proposes an ensemble learning method with multiple classifier types and diversity for condition-based maintenance in manufacturing industries, to address the bias problem when using only one base classifier type. Aside from manipulating data diversity, this method includes multiple classifier types, dynamic weight adjusting, and databased adaption to concept drifts for offline learning models, to promote precision of the forecasting model and precisely detect and adapt to concept drifts. With these features, the proposed method requires powerful computing resources to efficiently respond to practical condition-based maintenance applications. Therefore, the implementation of this method based on the MapReduce framework is proposed to increase computational efficiency. Simulation results show that this method can detect and adapt to all concept drifts with a high precision rate.

Joint Optimization of Preventive Maintenance and Spare Parts Inventory with Appointment Policy

Under the background of the wide application of condition-based maintenance (CBM) in maintenance practice, the joint optimization of maintenance and spare parts inventory is becoming a hot research to take full advantage of CBM and reduce the operational cost. In order to avoid both the high inventory level and the shortage of spare parts, an appointment policy of spare parts is first proposed based on the prediction of remaining useful lifetime, and then a corresponding joint optimization model of preventive maintenance and spare parts inventory is established. Due to the complexity of the model, the combination method of genetic algorithm and Monte Carlo is presented to get the optimal maximum inventory level, safety inventory level, potential failure threshold, and appointment threshold to minimize the cost rate. Finally, the proposed model is studied through a case study and compared with both the separate optimization and the joint optimization without appointment policy, and the results show that the proposed model is more effective. In addition, the sensitivity analysis shows that the proposed model is consistent with the actual situation of maintenance practices and inventory management.

Optimization of Aeroengine Shop Visit Decisions Based on Remaining Useful Life and Stochastic Repair Time

Considering the wide application of condition-based maintenance in aeroengine maintenance practice, it becomes possible for aeroengines to carry out their preventive maintenance in just-in-time (JIT) manner by reasonably planning their shop visits (SVs). In this study, an approach is proposed to make aeroengine SV decisions following the concept of JIT. Firstly, a state space model (SSM) for aeroengine based on exhaust gas temperature margin is developed to predict the remaining useful life (RUL) of aeroengine. Secondly, the effect of SV decisions on risk and service level (SL) is analyzed, and an optimization of the aeroengine SV decisions based on RUL and stochastic repair time is performed to carry out JIT manner with the requirement of safety and SL. Finally, a case study considering two CFM-56 aeroengines is presented to demonstrate the proposed approach. The results show that predictive accuracy of RUL with SSM is higher than with linear regression, and the process of SV decisions is simple and feasible for airlines to improve the inventory management level of their aeroengines.

Machine learning approaches for improving condition-based maintenance of naval propulsion plants

Availability, reliability and economic sustainability of naval propulsion plants are key elements to cope with because maintenance costs represent a large slice of total operational expenses. Depending on the adopted strategy, impact of maintenance on overall expenses can remarkably vary; for example, letting an asset running up until breakdown can lead to unaffordable costs. As a matter of fact, a desideratum is to progress maintenance technology of ship propulsion systems from breakdown or preventive maintenance up to more effective condition-based maintenance approaches. The central idea in condition-based maintenance is to monitor the propulsion equipment by exploiting heterogeneous sensors, enabling diagnosis and, most of all, prognosis of the propulsion system’s components and of their potential future failures. The success of condition-based maintenance clearly hinges on the capability of developing effective predictive models; for this purpose, effective use of machine learning methods is proposed in this article. In particular, authors take into consideration an application of condition-based maintenance to gas turbines used for vessel propulsion, where the performance and advantages of exploiting machine learning methods in modeling the degradation of the propulsion plant over time are tested. Experiments, conducted on data generated from a sophisticated simulator of a gas turbine, mounted on a Frigate characterized by a COmbined Diesel eLectric And Gas propulsion plant type, will allow to show the effectiveness of the proposed machine learning approaches and to benchmark them in a realistic maritime application.

Condition-Based Maintenance and State Diagnosis of Electric Equipment

Time-based maintenance is mainly used by power plants in our country. Through the study of the disadvantages of time-based maintenance, condition-based maintenance is proposed. This paper details the superiority and the concept of condition-based maintenance, the trigonometry fuzzy number theory and the fuzzy fault tree analysis have been proposed in the state diagnosis. This paper provides the reference to online monitoring and the levels of diagnosis. It’s a reliable plan that the application of condition-based maintenance to power plants.