Condition-based Maintenance System Research Articles

Holistic Concept Towards a Reference Architecture Model for Predictive Maintenance

In the era of digital transformation of factories, one of the most challenging applications of the Industrial Internet of Things (IIoT) is predictive maintenance. This paper presents a holistic concept for predictive maintenance together with a reference architecture that includes data acquisition on the sensor level, edge computing and digital twin applications. For that purpose, condition-based maintenance, lifecycle monitoring and digital assistance systems are integrated to develop application-specific digital twins based on the proposed architecture, integrating heterogenous data sources in order to enhance the accuracy of the machine learning models. The concept is illustrated through an experimental use case.

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.

The production schedule optimization of a multilayer superposed CBM system: An experimental study

There are obvious differences in the reservoir-forming mechanisms, flow mechanisms, and production trends between a multilayer superposed CBM system and a single CBM system. This paper performed large-scale physical simulations of CBM production in a multilayer superposed gas-bearing system. According to the experimental results, during commingled production, gas backfilling from a high initial reservoir pressure coal seam into a low reservoir pressure coal seam (LRPCM) occurred, which restrained or even shielded gas production from the low initial reservoir pressure coal seam. Progressive production effectively prevented gas backfilling by optimizing the gas production time of the coal seam, giving the coal seam a more reasonable productivity distribution. The permeability of the two production schedules showed obvious differences in the early stage, which gradually decreased in the middle and late stages. Different values of the coefficient D showed that progressive production provided a better production schedule.

Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines.

Induction machines (IMs) are critical components of many industrial processes, what justifies the use of condition-based maintenance (CBM) systems for detecting their faults at an early stage, in order to avoid costly breakdowns of production lines. The development of CBM systems for IMs relies on the use of fast models that can accurately simulate the machine in faulty conditions. In particular, IM models must be able to reproduce the characteristic harmonics that the IM faults impress in the spatial waves of the air gap magneto-motive force (MMF), due to the complex interactions between spatial and time harmonics. A common type of fault is the eccentricity of the rotor core, which provokes an unbalanced magnetic pull, and can lead to destructive rotor-stator rub. Models developed using the finite element method (FEM) can achieve the required accuracy, but their high computational costs hinder their use in online CBM systems. Analytical models are much faster, but they need an inductance matrix that takes into account the asymmetries generated by the eccentricity fault. Building the inductance matrix for eccentric IMs using traditional techniques, such as the winding function approach (WFA), is a highly complex task, because these functions depend on the combined effect of the winding layout and of the air gap asymmetry. In this paper, a novel method for the fast and simple computation of the inductance matrix for eccentric IMs is presented, which decouples the influence of the air gap asymmetry and of the winding configuration using two independent tensors. It is based on the construction of a primitive inductance tensor, which formulates the eccentricity fault using single conductors as the simplest reference frame; and a winding tensor that converts it into the inductance matrix of a particular machine, taking into account the configuration of the windings. The proposed approach applies routine procedures from tensor algebra for performing such transformation in a simple way. It is theoretically explained and experimentally validated with a commercial induction motor with a mixed eccentricity fault.

Planning Horizon and Its Effects on CBM System

Planning Horizon and Its Effects on CBM System

Degrader Analysis for Diagnostic and Predictive Capabilities: A Demonstration of Progress in DoD CBM+ Initiatives

Abstract This paper presents a modified reliability centered maintenance (RCM) methodology developed by The Applied Research Laboratory at The Pennsylvania State University (ARL Penn State) to meet challenges in decreasing life cycle sustainment costs for critical Naval assets. The focus of this paper is on the requirements for the development of the on-board Prognostics and Health Management (PHM) system with a discussion on the implementation progress for two systems: the high pressure air compressor (HPAC), and the advanced carbon dioxide removal unit (ACRU). Recent Department of Defense (DoD) guidance calls for implementing Condition Based Maintenance (CBM) as an alternative to traditional reactive and preventative maintenance strategies that rely on regular and active participation from subject matter experts to evaluate the health condition of critical systems. The RCM based degrader analysis utilizes data from multiple sources to provide a path for selecting systems and components most likely to benefit from the implementation of diagnostic and predictive capabilities for monitoring and managing failure modes by determining various options of possible CBM system designs that provide the highest potential ROI. Sensor data collected by the PHM system can be used with machine learning applications to develop failure mode predictive algorithms with greatest benefit in terms of performance, sustainment costs, and increasing platform operational availability. The approach supports traditional maintenance strategy development by assessing the financial benefit of the PHM technology implementation with promising potential for many industrial and military complex adaptive system applications.

Cost Analysis for IoT Based Condition Based Maintenance to Increase Productivity

Condition Based Maintenance (CBM) is one of maintenance method that believed to be the most effective at reduction of cost and number of activities than the other maintenance methods. Degradation monitoring condition is an important things in order to get effective CBM system. The rapid development in information and communication has lead our industrial phase to industry 4.0, where are lot of smart objects can be connected and integrated in one network system, which is called Internet of Things (IoT). IoT facilitates monitoring and controlling to an object, and help maintenance system to monitor, record, and analyze the degradation of the object. Furthermore, with real time monitoring, system could detect and make decision when is maintenance activity should be done with consideration in cost. This research focused in the issue of integration of many smart objects to support CBM activities in cost reduction and IoT decision making with cost minimization as criteria. Cost analysis has been done using Activity Based Costing (ABC) and mathematical model has been constructed for decision making criteria which will be tested with numerical test using the data that gathered from company which applied IoT system. There are three condition which are tested: system without IoT implementation, current system with IoT implementation (auto shutdown when machine stroke reaches 300), and IoT system that consider the degradation condition to shut down. The result shows that IoT based CBM system that consider degradation level will incur optimal number of activities which resulting in fewer cost that the other systems. With fewer activity maintenance than auto-shutdown at 300 strokes, shows that the productivity increase without any delay due to maintenance

A novel deep learning method based on attention mechanism for bearing remaining useful life prediction

Rolling bearing is a key component in rotation machine, whose remaining useful life (RUL) prediction is an essential issue of constructing condition-based maintenance (CBM) system. However, recent data-driven approaches for bearing RUL prediction still require prior knowledge to extract features, construct health indicate (HI) and set up threshold, which is inefficient in the big data era. In this paper, a pure data-driven method for bearing RUL prediction with little prior knowledge is proposed. This method includes three steps, i.e., features extraction, HI prediction and RUL calculation. In the first step, five band-pass energy values of frequency spectrum are extracted as features. Then, a recurrent neural network based on encoder–decoder framework with attention mechanism is proposed to predict HI values, which are designed closely related with the RUL values in this paper. Finally, the final RUL value can be obtained via linear regression. Experiments carried out on the dataset from PRONOSTIA and comparison with other novel approaches demonstrate that the proposed method achieves a better performance.

Improving maintenance quality in airport baggage handling operations

Purpose: The findings of a recent study at Heathrow are presented. The objective of the study was an operational quality issue with the baggage reclaim process and addressed the problem of unexpected downtime of baggage carousels during operational hours. For airlines and airports, the reclaim carousel is a key element in the process whereby passengers decide about the quality of their journey. Failure that leads to longer waiting times or even the relocation of baggage to another carousel results in passenger dissatisfaction and therefore needs to be avoided. The current regime of ‘time-based’ or ‘preventative’ maintenance can be classified as ‘run-to-break’ or ‘reactive’, causing frequent and costly downtime. A novel condition-based monitoring method to improve the reliability of the time-critical baggage reclaim process is described. Reclaim carousel maintenance quality was improved by the development of innovative condition-based maintenance systems designed to meet the requirements of twenty-first century airport systems and Industry 4.0 in cooperation with Siemens DF (digital factory) and their internet of things (IoT) platform Mindsphere. Methodology: A technical action research approach was undertaken at one of the biggest capital airport baggage handling systems in Europe. From June 2016 a condition monitoring pilot system on one operational carousel was established using engineering cycle theory. A solution was designed, installed, continually monitored and the results discussed with practitioners from the operation and maintenance (O&M) department. Root cause analysis was used to identify reasons for abrasive wear, followed by failure simulations during operation. Technical vibration data was collected so that an adequate condition monitoring system could be developed and optimised that improved the maintenance quality of reclaim carousels and reduced the costs associated with unexpected failure of the baggage handling system. Findings: Run-to-break maintenance is never conducive to quality maintenance and control and results in great uncertainty and unreliability. Meeting the objective of improving quality was difficult because the assets are hidden and baggage carousels are in constant use; however using wireless beacon technology it was possible to identify what problems might occur and when. Using the cloud-based IoT and Airport 4.0 also necessitated many sophisticated checks and measures, which are now in development. This study highlights the value of changing from the antiquated ‘run to break’ maintenance of hidden assets in airport baggage handling carousels to high-quality maintenance through the use of condition monitoring using wireless vibration sensors linked to a cloud-based IoT ecosystem. Originality/Value: With this solution in operation, maintenance quality has been approved and heavy damage can be avoided. The solution addresses problems caused by a variety of hidden equipment needing the highest possible maintenance quality. It can instantly secure the best quality of service for critical assets in an operation that cannot afford any unplanned downtime.

Innovative airport 4.0 condition-based maintenance system for baggage handling DCV systems

PurposeThe purpose of this paper is to present the findings of a recent study conducted with the objective of addressing the problem of failure of baggage carts in the high-speed baggage tunnel at Heathrow Terminal 5 by the development of an innovative condition-based maintenance (CBM) system designed to meet the requirements of 21st century airport systems and Industry 4.0.Design/methodology/approachAn empirical experimental approach to this action research was taken to install a vibration condition monitoring pilot test in the north tunnel at Terminal 5. Vibration data were collected over a 6-month period and analysed to find the threshold of good quality tyres and those with worn bearings that needed replacement. The results were compared with existing measures to demonstrate that vibration monitoring could be used as a predictive model for CBM.FindingsThe findings demonstrated a clear trend of increasing vibration velocity with age and use of the baggage cart wheels caused by wheel mass unbalanced inertia that was transmitted to the tracks as vibration. As a result, preventative maintenance is essential to ensure the smooth running of airport baggage. This research demonstrates that a healthy wheel produces vibration of under 60 mm/s whereas a damaged wheel measures up to 100 mm/s peak to peak velocity and this can be used in real-time condition monitoring to prevent baggage cart failure. It can also run as an autonomous system linked to AI and Industry 4.0 airport logic.Originality/valueWhilst vibration monitoring has been used to measure movement in static structures such as bridges and used in rotating machinery such as railway wheels (Tondon and Choudhury, 1999); this is unique as it is the first time it has been applied on a stationary structure (tracks) carrying high-speed rotating machinery (baggage cart wheels). This technique has been patented and proven in the pilot study and is in the process of being rolled out to all Heathrow terminal connection tunnels. It has implications for all other airports worldwide and, with new economic sensors, to other applications that rely on moving conveyor belts.

Condition monitoring for airport baggage handling in the era of industry 4.0

PurposeThe purpose of this paper is to present the findings of a recent study conducted with the objective of addressing the problem of failure of baggage carts in the high-speed baggage tunnel at Heathrow Terminal 5 by the development of an innovative condition-based maintenance system designed to meet the requirements of twenty-first century airport systems and Industry 4.0.Design/methodology/approachAn empirical experimental approach to this action research was taken to install a vibration condition monitoring pilot test in the north tunnel at Terminal 5. Vibration data were collected over a 6-month period and analysed to find the threshold of good quality tires and those with worn bearings that needed replacement. The results were compared with existing measures to demonstrate that vibration monitoring could be used as a predictive model for condition-based maintenance.FindingsThe findings demonstrated a clear trend of increasing vibration velocity with age and use of the baggage cart wheels caused by wheel mass unbalanced inertia that was transmitted to the tracks as vibration. As a result, preventative maintenance is essential to ensure the smooth running of airport baggage. This research demonstrates that a healthy wheel produces vibration of under 60 mm/s whereas a damaged wheel measures up to 100 mm/s peak-to-peak velocity and this can be used in real-time condition monitoring to prevent baggage cart failure. It can also run as an autonomous system linked to AI and Industry 4.0 airport logic.Originality/valueWhilst vibration monitoring has been used to measure movement in static structures such as bridges and used in rotating machinery such as railway wheels (Tondon and Choudhury, 1999) this is unique as it is the first time it has been applied on a stationary structure (tracks) carrying high-speed rotating machinery (baggage cart wheels). This technique has been patented and proven in the pilot study and is in the process of being rolled out to all Heathrow terminal connection tunnels. It has implications for all other airports world-wide and, with new economic sensors, to other applications that rely on moving conveyor belts.

Combinatorial Optimization in Condition Based Maintenance System

Combinatorial Optimization in Condition Based Maintenance System

Increasing the lifetime of wind turbine blades by CBM in a dusty region

The maintenance and shutdown costs of a wind turbine blade have a direct impact on the total production and can represent a loss of as much as 25% of the energy generated. Establishing appropriate condition-based maintenance (CBM) which involves monitoring system becomes a major need for wind turbines located in a dusty region. CBM will considerably reduce the costs and the number of maintenance interventions, knowing that in a dusty region, the blades are submitted to erosive wear which contributes to the modification of the aerodynamic shape of the leading edge, and hence reduce the performance of the wind turbine. This paper shows that before implementing a CBM system, it is necessary to understand the different mechanisms of erosive wear. The experiments carried out show that the installation of this system is very effective in detecting any degradation caused by erosive wear on the surface of a blade during its service.

The Spatial Distribution of CBM Systems under the Control of Structure and Sedimentation: The Gujiao Block as an Example

A multilayer coal bed methane system with a complex superimposed relationship developed vertically in the Gujiao block. Taking the cumulative thickness of mudstone between neighbouring coal seams and open faults as object of research, the key strata for the division of different CBM systems was defined. The connection of an open fault for the fluid pressure system and the spatial distribution of the CBM system were analysed. The results showed that the porosity of compact mudstone is extremely low. When the mudstone thickness of the roof and floor increases, the gas content increases. Taking the Lijiashe and Wangzhimao faults as the boundary lines, the fluid pressure gradient between the Shanxi and Taiyuan formations is less than 0.08 MPa /100 m in the northern fracture zone, such as the Zhenchengdi, the Xiqu, the Tunlan, and the northern Dongqu well fields. The vertical gas units of different strata may belong to the same CBM system. The structure is simple in the southern Lijiashe fault. The vertical gas units in the southern Lijiashe fault belong to different CBM systems. Combined with the minimum thickness of the mudstone layer (2 m) and coal seam (0.5 m) standards, the spatial distribution of CBM systems was analysed.

해양 플랜트 Mooring Line 고장예지 방안에 관한 연구

The failure of a mechanical system could lead to enormous social and economic damage. To address this problem, companies have carried out various maintenance policies such as corrective maintenance, preventive maintenance, risk-based maintenance, and so on. Recently, thanks to the emerging information and communication technologies such as internet-of-things, wireless tele-communication technologies, and so on, the condition-based maintenance (or predictive maintenance, prognostics and health management) policy has been highlighted. The condition-based maintenance could take the suitable maintenance action before a failure occurs based on the condition of the target system. In general, the condition-based maintenance system has the capability to monitor the state of the mechanical system in real time, and detect the system abnormality at an early stage, and predict the future occurrence of failure in advance. To this end, it is critical to develop the suitable monitoring, diagnostics, and prognostics methods for the target system. In this study, we will propose an approach to predict RUL (Remaining Useful Lifetime) of offshore plant equipment (mooring line) based on gathered sensor data. To predict the RUL, several time-series forecasting methods have been applied and evaluated. In conclusion, we have summarized our study and discussed the limitations of the proposed approach.

A study of the development of a condition-based maintenance system for an LNG FPSO

In general, the equipment failures or accidents of offshore plants during operation and maintenance (O&M) period cause catastrophic damage. Thus, it is necessary to undertake proactive maintenance in advance in order to avoid abnormal situations. Currently, owing to the emergence of information communication technologies (ICTs) and sensor technologies, it is possible to gather the health status data of important equipment and use this information for maintenance during the O&M period. It sheds light on condition-based maintenance (CBM) strategy. In this study, we introduce a case study on the development of a CBM system (CBMS) for an oil and gas offshore plant, i.e. liquefied natural gas floating production storage and offloading vessel (LNG FPSO). The study includes the introduction of the system architecture, main components, diagnostics and prognostics methods of the system, as well as a discussion of its implementation.

A study on the real-time reliability of on-board equipment of train control system

Real-time reliability evaluation is conducive to establishing a condition based maintenance system for the purpose of guaranteeing continuous train operation. According to the inherent characteristics of the on-board equipment, the connotation of reliability evaluation of on-board equipment is defined and the evaluation index of real-time reliability is provided in this paper. From the perspective of methodology and practical application, the real-time reliability of the on-board equipment is discussed in detail, and the method of evaluating the realtime reliability of on-board equipment at component level based on Hidden Markov Model (HMM) is proposed. In this method the performance degradation data is used directly to realize the accurate perception of the hidden state transition process of on-board equipment, which can achieve a better description of the real-time reliability of the equipment.

A flexible distribution and its application in reliability engineering

Probability distributions of random variables are necessary for reliability evaluation. Generally, probability distributions are determined using one or two parameters evaluated from the mean and standard deviation of statistical data. However, these distributions are not sufficiently flexible to represent the skewness and kurtosis of data. This study therefore proposes a probability distribution based on the cubic normal transformation, whose parameters are determined using the skewness and kurtosis, as well as the mean and standard deviation of available data. This distribution is categorized into six different types based on different combinations of skewness and kurtosis. The boundaries of each type are identified, and the completeness of each type is proved. The cubic normal distribution is demonstrated to provide significant flexibility, and its applicable range covers a large area in the skewness–kurtosis plane, thus enabling it to approximate well-known distributions. The distribution is then applied in reliability engineering: simulating distributions of statistical data, calculating fourth-moment reliability index, finding optimal inspection intervals for condition-based maintenance system, and assessing the influence of input uncertainties on the whole output of a system. Several examples are presented to demonstrate the accuracy and efficacy of the distribution in the above-mentioned reliability engineering practices.

Comparison of Automated Feature Selection and Reduction methods on the Condition Monitoring issue

Condition Monitoring is a key task of condition-based maintenance (CBM). The economic efficiency together with the reliability of CBM is expanding its use to areas where it was not used before. However, this expansion of CBM systems is ballasted by two main obstacles: the maintenance related knowledge of recently monitored assets, which is usually scarce and not structured; and the lack of failure related data, which is a major obstacle of data-driven methods. With the aim of developing a method to automatically select and/or reduce features, this work compares widely used dimensionality reduction and feature selection methods, which are capable of automatically obtaining knowledge while the monitoring is carried out. Those methods can later be used to help operators decide which features could be more related to the degradation of the machine. The purpose of this work is to determine which of those compared methods could be more appropriate for detecting anomalies in order to develop monitoring systems.

Condition-based maintenance for systems with economic dependence and load sharing

Redundancy is often essential for achieving high system availability. An additional benefit of installing redundant components is that the total system load can be shared among components, thus preventing fast deterioration. On the one hand, this provides an incentive to replace failed components as soon as possible, as a component failure increases the load on the remaining components. On the other hand, however, redundancy gives rise to maintenance clustering and postponement opportunities, to reduce the maintenance frequency and thereby lower downtime and maintenance set-up costs. To date, this trade-off has not been investigated under a condition-based maintenance regime. To address this, we consider a parallel system that is subject to both failure dependence through load sharing and economic dependence through maintenance set-up costs. We formulate our system as a Markov Decision Process, and obtain the optimal replacement decisions that minimize the long-run average cost per time unit. Through a numerical investigation and a sensitivity analysis, in which we vary the degree of load sharing, and the maintenance set-up cost, and the degradation process, we obtain key insights into the optimal policy structure. Standard threshold policies, that replace a component as soon as its deterioration exceeds a certain threshold, can be far from optimal, while ignoring or misinterpreting the load sharing effects between components can also lead to a significantly more expensive maintenance policy.