Condition-based Maintenance System Research Articles

On the Study of the Establishment of Condition Based Maintenance System for Weapon Systems

On the Study of the Establishment of Condition Based Maintenance System for Weapon Systems

Comprehensive Review of Machine Learning Techniques for Condition-Based Maintenance

While most industrial maintenance strategies are centered on optimizing machine runtime and cost reduction, the condition-based maintenance (CBM) strategy distinguishes itself from others in its use of real-time operational data from machines to help engineers make informed decisions. The introduction of machine learning (ML) into a CBM strategy can increase its effectiveness, enabling more accurate predictions and making the decision-making process more efficient. In this review paper, we seek to provide a comprehensive overview of the role ML plays in modern CBM systems, beginning by outlining the core concepts and historical development of CBM and briefly introducing various ML techniques being employed in industry today. We then review numerous real-world cases where ML-based CBM systems have been implemented and discuss some of the technological, human, and ethical challenges faced by organizations seeking to integrate sophisticated ML models into existing CBM systems. We end by highlighting some of the current limitations of ML-based CBM systems, paving the way for a discussion on emerging trends and future research directions in this area.

Remaining Useful Life Prediction Of Precision Bearing Based on Multi-Head Attention Mechanism

The high-precision bearing is the core component of the space actuator, whose remaining useful life (RUL) prediction is an essential issue of constructing condition-based maintenance (CBM) system. To address the current challenges of establishing traditional physical failure models for high-precision bearings and dealing with long time-series vibration data, this paper proposes a precision bearing remaining useful life prediction model based on multi-head attention mechanism. The model utilizes a one-dimensional convolutional neural network (CNN) to compress the input features along the time dimension, then, an encoder-decoder architecture with a multi-head attention mechanism for further feature exploration. Experiments are conducted using vibration signals from the IEEE PHM 2012 Challenge dataset for training and validation. The results indicate that this algorithm exhibits relatively smaller relative errors compared to other algorithms, demonstrating good accuracy and strong stability. This approach shows promise in accurate and reliable precision bearing life prediction.

Field Validation of a Universally Applicable Condition-Based Maintenance System for Mud Pumps

Summary Although mud pumps are considered to be critical rig equipment, their health monitoring currently still relies on infrequent human observation and monitoring. This approach often fails to detect pump damage at an early stage, resulting in nonproductive time (NPT) and increased well construction costs when initial damage progresses and pumps go down unexpectedly and catastrophically. Automated approaches to condition-based maintenance (CBM) of mud pumps to date have failed due to the lack of a generalized solution applicable to any pump type and/or operating conditions. This paper presents a field-validated universally applicable solution to mud pump CBM. The system uses a sensor package that includes acoustic emission sensors and accelerometers in combination with anomaly detection deep learning data analysis to pinpoint any abnormal behavior of the pump and its components. The deep learning models are trained with undamaged normal state data only, and a damage score characterizing the extent of damage to the mud pump is calculated to identify the earliest signs of damage. The system can then generate alerts to notify the rig crew of the damage level of key mud pump components, prompting proactive maintenance actions. Field tests were conducted while drilling an unconventional shale well in west Texas, USA, and a geothermal well in Japan (i.e., two very different drilling operations) to verify the feasibility and general applicability of the developed pump CBM solution. Sensors were attached to pump modules, and data were collected and analyzed using the deep learning models during drilling operations. During the field tests, different hyperparameters and features were compared to select the most effective ones for identifying damage while at the same time delivering low false positive rates (i.e., false alarms during normal state pump operation). The system required only several hours of normal state data for training with no prior pump information. Moreover, it correctly identified the degradation of the pump, swabs, and valves and produced early alerts several hours (in the range of 0.5–17 hours) before actual pump maintenance action was taken by the rig crew. This generally applicable pump CBM system eliminates the environmental, health, and safety concerns that can occur during human-based observations of mud pump health and avoids unnecessary NPT associated with catastrophic pump failures. The final version of this system will be a fully self-contained magnetically attachable box containing sensors and a processor, generating simple indicators for recommending proactive pump maintenance tasks when needed.

Reliability-based leading edge erosion maintenance strategy selection framework

Leading edge erosion has become one of the most prevailing failure modes of wind turbines. Its effects can evolve from an aerodynamic modification of the properties of the blade to a potential structural failure of the leading edge. The first produces a reduction of energy production and the second can produce a catastrophic failure of the blade. Considering the uncertainties and constraints involved in the design of optimal operation and maintenance (O&M) strategies for offshore assets and the influence of site-specific parameters on the dynamics of this particular failure mode, the task becomes complex. In this study, a framework to evaluate the influence of different maintenance strategies considering uncertainties in weather, material behaviour and repair success is presented. Monte Carlo Simulation (MCS) is used alongside a computational framework for Leading Edge Erosion (LEE) degradation to evaluate the lifetime cost distribution and probability of failure of the chosen maintenance strategies. The use of the framework is demonstrated in a case study considering a 5-MW offshore wind turbine located in the north of Germany. The influence of the modification of the maintenance interval or time between repairs and the comparison with maintenance activities executed only during months with milder weather is analysed in terms of cost and reliability. A Pareto front plot considering the probability of failure and the median of the cost is used to jointly compare strategies considering both aspects to provide a tool for risk-informed maintenance selection. Finally, the potential benefits of condition-based maintenance and autonomous decision-making systems are discussed. The case of study shows the benefits of repairs during summer months and the importance of the relation risk/O&M cost for different maintenance strategies.

Using Non-stationary Time-Domain Statistical Measures for Predictive Maintenance of Large Centrifugal Compressors in the Absence of Component Faults

Time-domain statistical measures such as standard deviation, skewness, and kurtosis are simple yet effective tools for assessing the health of rotating machinery based on measured vibration signals. These statistical quantities are, therefore, widely used in industry when designing condition-based maintenance (CBM) systems, especially for rotating machinery. Despite the growing adoption of CBM systems, determining the overhaul schedule can still be challenging, particularly when the rotating machinery in question does not exhibit any fault symptoms. As a result, the overhaul schedule is often determined using the traditional time-based maintenance (TBM) approach. In this short communication, we examine the time-varying characteristics of statistical quantities immediately before and after the overhaul of a large centrifugal compressor. It was found that the non-stationarity of the standard deviation increases in cases where an overhaul is due, while the variability of skewness and kurtosis does not change significantly in the absence of specific fault components.

Impact of community-based micro-finance in urban poverty reduction in Bangladesh

Purpose: This study aimed to assess the impact of community-based microfinance in reducing urban poverty with a particular focus on Sylhet City, Bangladesh. Methodology: The Community Saving & Credit (CSC) strategy of the Urban Partnership for Poverty Reduction project was chosen as a case in this study. This study used a qualitative research approach by following focus group discussions and in-depth interviews as data collection instruments. The purposive sampling method has been observed to collect data from 20 (twenty) respondents from Sylhet City Corporation's two slum areas, where the Urban Partnerships for Poverty Reduction Project was implemented like other urban areas of Bangladesh. Findings: The study findings show that implementing the Community Saving and Credit (CSC) strategy in Sylhet City has reduced poverty among those who applied for loans. It has enhanced the availability of food, made housing more affordable, diversified income sources, improved health conditions, increased access to education, and amplified the voice of women, especially those in poverty. Research implications: This research will help the government to achieve the Sustainable Development Goals (SDGs). It will also inform the government about local perceptions regarding the dimensions of escaping poverty. After knowing about the locally specific dimensions of getting out of poverty and the effect of the CBM system in reducing poverty, the government and the policymakers can plan additional steps to operate community-based microcredit systems to reduce poverty.

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.

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.

Additive manufacturing process design for complex aircraft components

The process of metal additive manufacturing (AM) is now widely used in fabricating complex parts in today’s industry. The scope of this paper is to redesign a manufacturing process for complex aircraft components using wing ribs as example by considering embedded Internet of Things (IoT) sensory capability that can be used in an Industry 4.0 ecosystem for moving away from a condition-based preventive maintenance system to a data-driven predictive maintenance-based system. This work is based on a previous study that considered the part design stage which deals with finding the best design solution for a single part. Considering a wing rib geometry of 3-mm web thickness with 6-mm upper and lower caps, the manufacturing process is designed and assessed using the Simufact Additive™ software. The use of AM when embedding IoT sensors allows more flexibility without compromising the structural integrity of parts, as well as the advantage of design freedom and limited cost when modifying geometries. The outcomes show that the manufacturing process depends strongly on hot isostatic pressing (HIP) for the wing rib, but for the sensory covers it presented no significant improvement. The results also show that the support optimisation can lead to an important reduction of mass and volume as well as an improvement of the structural performance.

A Study on the Application of Discrete Wavelet Decomposition for Fault Diagnosis on a Ship Oil Purifier

With the development of the Internet of things, big data, and AI leading the 4th industrial revolution, it has become possible to acquire, manage, and analyze vast and diverse condition signals from various industrial machinery facilities. In addition, it has been revealed that various and large amounts of signals acquired from the facilities can be utilized for fault diagnosis. Currently, while data-driven fault diagnosis techniques applicable to the facilities are being developed, it has been tried to apply the techniques for the development of fully autonomous ships in the shipbuilding and shipping industry. Since the autonomous ships must be able to detect and diagnose the failures on their own in real time, the overall research is required on how to acquire signals from the ship facilities and use them to diagnose their failures. In this study, a fault diagnosis framework was proposed for condition-based maintenance (CBM) of ship oil purifiers, which are an auxiliary facility in the engine system of a ship. First, an oil purifier test-bed for simulating faults was built to obtain data on the state of the equipment. After extracting features using discrete wavelet decomposition from the data, the features were visualized by using t-distributed stochastic neighbor embedding, and were used to train support vector machine-based diagnostic models. Finally, the trained models were evaluated with Accuracy and F1 score, and some models scored 0.99 or higher, confirming high diagnostic performance. This study can be used as a reference for establishing CBM system and fault diagnosis system. Furthermore, this study is expected to improve the safety and reliability of oil purifiers in Degree 4 MASS.

A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump

An efficient Remaining Useful Life (RUL) prediction method is one of the most important features of a condition-based maintenance system. A running machine’s RUL prognosis in its real-time is a challenging task, especially when there is no historic failure data available for that particular machine. In this paper, an online RUL of an in-operation industrial slurry pump having no historical failure data has been predicted. At first, the available raw vibration datasets were filtered out for valid datasets. The obtained valid datasets were utilized for constructing the Health Degradation Trends (HDTs) using principal component analysis and a moving average method. Then, a novel procedure for automatically selecting the HDT’s data points for initiating the iteration process of prediction was formulated. Afterward, a hybrid deep LSTM model embedded with a smart learning rate mechanism was developed for estimating the online RUL using the selected points of HDTs. The online RUL prediction results produced by the developed model were quite satisfactory when they were compared with other online RUL prediction methods.

Analytical Model of Eccentric Induction Machines Using the Conformal Winding Tensor Approach.

Induction machines (IMs) are a critical component of many industrial processes, and their failure can cause large economic losses. Condition-based maintenance systems (CBMs) that are capable of detecting their failures at an incipient stage can reduce these risks by continuously monitoring the IMs’ condition. The development and reliable operations of CBMs systems require rapid modeling of the faulty IM. Due to the fault-induced IM asymmetries, these models are much more complex than those used for a healthy IM. In particular, a mixed eccentricity fault (static and dynamic), which can degenerate into rubbing and destruction of the rotor, produces a non-uniform IM air gap that is different for each rotor position, which makes its very difficult to calculate the IM’s inductance matrix. In this work, a new analytical model of an eccentric IM is presented. It is based on the winding tensor approach, which allows a clear separation between the air gap and winding-related faults. Contrary to previous approaches, where complex expressions have been developed for obtaining mutual inductances between conductors and windings of an eccentric IM, a conformal transformation is proposed in this work, which allows using the simple inductance expressions of a healthy IM. This novel conformal winding tensor approach (CWFA) is theoretically explained and validated with the diagnosis of two commercial IMs with a mixed eccentricity fault.

Research on Prognostic and Health Management of Inertial Navigation System

Abstract Prognostic and health management (PHM) system significantly reduces costs of maintenance, utilization, and assurance, and improves safety and availability of aircraft. By drawing on and absorbing foreign advanced experience, this paper deeply studies the key technology of PHM, and combines the specific features of the inertial navigation system to determine the program flow of the PHM system. This paper is described PHM technical related elements, which can be reformed the design of Inertial Navigation System and can push the development of the condition-based maintenance and PHM system.

Development of an advanced condition-based maintenance system for high-critical industrial fans in a foundry

In the recent years, manufacturing companies are investing in sensors and information systems to implement condition-based maintenance (CBM), thus pursuing the benefits of digital transformation. Nevertheless, to implement CBM as advanced digital system, significant investment should be made to gather and manage all needed data from different sources; besides, qualified human resources are required for data analytics. Given this premise, the present paper aims at describing an industrial project where an advanced CBM system for high-critical industrial fans is implemented in a foundry. Indeed, the goal is to use already available data from the extant automation and additional vibration data to develop state detection and to identify any abnormal behaviour of the assets. The evidence from the project is that: i) the vibration analysis remains an easy and cost-effective, yet well-performing way, to monitor the state and the health of machines with rotating components; ii) automatic regulation system may mask the underlying behaviour and degradation of complex assets; iii) already gathered data from extant automation are mainly focused on the process parameters and provides an aid to describe the working state of the assets, but have limited potentialities for novelty detection. Eventually, the paper envisions future development of a more integrated approach aimed at a combined elaboration of data from the extant automation and vibration data. The integrated approach is under development, hence the paper provides insights on the on-going analyses.

A condition based maintenance system for important auxiliary equipment of thermal power unit

Through the research and development of key technologies of condition-based maintenance for power generation equipment, a set of important auxiliary equipment condition-based maintenance system for thermal power unit has been developed, which realizes the functions of integrated display of equipment-related data, equipment modeling configuration tool, equipment condition monitoring, equipment deterioration trend warning, equipment fault diagnosis, equipment health status evaluation, maintenance decision-making and so on. By studying the comprehensive assessment model of health state of power generation equipment, the equipment can be analyzed and evaluated scientifically and comprehensively, and the health state of equipment can be controlled step by step, thus improving the reliability of equipment operation.

Optimal State Risk Scheduling Based on Selective Maintenance Strategy

Because stochastic fault cases (i.e., opportunistic maintenance strategy) of the equipment are not considered in the condition-based maintenance decision of the system, which will deviate from the actual situation, a system condition-based maintenance scheduling model considering opportunistic maintenance strategy is proposed in this paper. To implement the system maintenance strategies, the correlation set is formulated by considering the relationship among different equipment. According to renewal process theory, the availability of the correlation set considering planned maintenance and opportunity maintenance is deduced and the maintenance strategy of the system is realized. Then, to reflect the relationship between equipment maintenance and system operation, a system state scheduling model aiming at minimizing the sum of maintenance risk and failure risk as well as considering system resource constraints is proposed, thus obtaining the optimal maintenance schedule based on system state. Finally, a simple test system and IEEE-RTS79 node system are employed to demonstrate the feasibility and effectiveness of the proposed maintenance model, and it also verified that the proposed model can be integrated into the power system condition-based maintenance theory.

Smart Mechanical Systems for Manufacturing in the Era of Industry 4.0: Condition-Based Predictive Maintenance and Dynamic System Modification for Small and Medium-Sized Enterprises

We are in the era of the fourth industrial revolution. Which highlights adaptability, monitoring, digitisation and efficiency in manufacturing as a result of the design of new smart mechanical systems. A central role in Industry 4.0 is played by maintenance and, within this framework, we define and review condition-based predictive maintenance. Thereafter, we propose a new class of smart mechanical systems that self-optimise utilising both condition-based maintenance and dynamic system modification. Akin to smart structures, smart mechanical systems will recognise and predict faults or malfunctions and, subsequently, self-optimise to restore desirable system behaviour. Potential benefits include increased reliability and efficiency while reducing cost without the requirement of highly skilled technicians. Thus, small and medium-sized enterprises are a specific target of such technology due to their increasing level of automatisation within Industry 4.0.

A maintenance scheduling optimization model for a multi-component machine in a digitalized manufacturing context

Abstract Manufacturing systems are facing a digital transformation that supports data-driven decision making and the improvement of the efficiency of condition-based or predictive maintenance systems. In this context, international standards and previous works related to the architecture of condition-based maintenance (CBM) systems are available, and the interest in maintenance scheduling optimization, triggered by failure predictions, are increasing. This work is framed within the scope of the PICOePRO project and considers a critical multi-component welding machine. This paper first provides a predictive maintenance architecture, coherent with the OS A-CBM standard and proposed within the project, and then develops one of the modules of this system, in which the main component is a maintenance scheduling optimization model. The defined architecture allows real-time decision-making, and the proposed model minimizes the machine maintenance cost, exploring the potential for cost-saving by grouping maintenance and using machine downtime to perform maintenance activities. The functioning of the module and the potential gains from the adoption of the developed model are illustrated using a numerical example. The architecture and the model provide a viable approach to guide maintenance decision-making in the era of digitalized manufacturing.

Unsupervised Learning Based Diagnosis Model for Anomaly Detection of Motor Bearing with Current Data

Lifecycle engineering is a key concept for promoting environmentally sustainable practices among manufacturing firms. A key aspect of life cycle management for pursuing sustainability is condition-based maintenance system that uses data analytics process such as anomaly detection to understand machine condition. Conventional machine learning technique applied to anomaly detection uses classifiers based on supervised learning to detect anomalies of motor bearings using the bearing specifications. However, supervised learning generally requires a large volume of data at the time of abnormal operation, which takes time to acquire. This paper therefore proposes a data analytics process to detect motor bearing failure using data during normal condition. In the data analytics process, we first make a power spectrum from current sensor signals. Then, after grouping a power spectrum into bins, we employ a Gaussian Mixture Model (GMM) to learn the normal condition of a motor bearing. Next, we calculate likelihood with the GMM to check for any difference from the normal condition. Experimental results demonstrate that the proposed method can show increasing anomaly of a bearing condition corresponding to insufficient grease.