The company CrossControl develops display computers for control systems in industrial vehicles which operate in rough environments. Currently, the system can detect and diagnose different faults but CrossControl would also like to predict upcoming failures by using Condition-Based Maintenance (CBM). CBM is a cost effective maintenance strategy where the health condition of the system is monitored and maintenance is only performed after a degradation in performance has been observed. This thesis work aims to investigate the possibilities of implementing CBM on CrossControl's system by studying the theory behind CBM and associated methods and by analysing real data from the system of one of CrossControl's customers. The results presented in this thesis consist of two literature studies and one case study. The first literature study introduces different types of maintenance and gives a detailed explanation of CBM. The second literature study contains a collection of methods used to estimate the Remaining Useful Life (RUL) of the system, which is an important step in CBM. The case study considers the twistlocks of Bromma Conquip's spreader system and serves as an example of how CBM can be used in practise and exemplifies some difficulties that can be encountered when implementing CBM. Finally, a discussion of the obtained results and some suggestions for future work and ideas for how CBM can be implemented on CrossControl's system are given.

<p>In order to stay competitive, it is necessary for companies to continuously increase the effectiveness and efficiency of their production processes. High availability has, thus, increased in importance. Therefore, maintenance has gained in importance as a support function for ensuring, e.g., quality products and on-time deliveries. Maintenance, though, is a costly support function. It has been reported that as much as 70% of the total production cost can be spent on maintenance. Further, as much as one-third of the cost of maintenance is incurred unnecessarily due to bad planning, overtime cost, limited or misused preventive maintenance, and so on. In so, condition based maintenance is introduced as one solution for a more effective maintenance.</p><p>In condition based maintenance, critical item characteristics are monitored in order to gain early indications of an incipient failure. Research, though, has shown that condition based maintenance has not been implemented on a wide basis. Therefore, the purpose of this research is to investigate how a condition based maintenance approach can be implemented in an industrial setting, and to develop a method that can assist companies in their implementation efforts. Further, the research has been divided in three research questions. They focus on: constituents of a condition based maintenance approach, decision-making prior implementation of condition based maintenance, and finally, the implementation of condition based maintenance in a company.</p><p>By using a systems approach and a case study process, how condition based maintenance can be implemented as a routine has been investigated. The result is an implementation method in which four suggested phases are presented. The method starts with a feasibility test. It then continues with an analysis phase, an implementation phase, and an assessment phase. The conclusions can be summarized as follows: implementing condition based maintenance consists of many general enabling factors, including management support, education and training, good communication, and motivation etc.</p>

The maintenance cost for an engineering system is an uncertain quantity due to uncertainties associated with occurrence of failure and the time taken to restore the system. The problem of probabilistic analysis of maintenance cost can be modeled as a stochastic renewal-reward process, which is a complex problem. Assuming that the time horizon of the maintenance policy approaches infinity, simple asymptotic formulas have been derived for the failure rate and the cost per unit time. These asymptotic formulas are widely utilized in the reliability literature for the optimization of a maintenance policy. However, in the finite life of highly reliable systems, such as safety systems used in a nuclear plant, the applicability of asymptotic approximations is questionable. Thus, the development of methods for accurate evaluation of expected maintenance cost, failure rate, and availability of engineering systems is the subject matter of this thesis. In this thesis, an accurate derivation of any m-th order statistical moment of maintenance cost is presented. The proposed formulation can be used to derive results for a specific maintenance policy. The cost of condition-based maintenance (CBM) of a system is analyzed in detail, in which the system degradation is modeled as a stochastic gamma process. The CBM model is generalized by considering the random repair time and delay in degradation initiation. Since the expected cost is not informative enough to estimate the financial risk measures, such as Value-at-Risk, the probability distribution of the maintenance cost is derived. This derivation is based on an interesting idea that the characteristic function of the cost can be computed from a renewal-type integral equation, and its Fourier transform leads to the probability distribution. A sequential inspection and replacement strategy is presented for the asset management of a large population of components. The finite-time analyses presented in this thesis can be combined to compute the reliability and availability at the system level. Practical case studies involving the maintenance of the heat transport piping system in a nuclear plant and a breakwater are presented. A general conclusion is that finite time cost analysis should be used for a realistic evaluation and optimization of maintenance policies for critical infrastructure systems.

This dissertation develops a new methodology and associated solution tools to achieve optimal operations and maintenance strategies for wind turbines, helping reduce operational costs and enhance the marketability of wind generation. The integrated framework proposed includes two optimization models for enabling decision support capability, and one discrete event-based simulation model that characterizes the dynamic operations of wind power systems. The problems in the optimization models are formulated as a partially observed Markov decision process to determine an optimal action based on a wind turbine's health status and the stochastic weather conditions. The rst optimization model uses homogeneous parameters with an assumption of stationary weather characteristics over the decision horizon. We derive a set of closed-form expressions for the optimal policy and explore the policy's monotonicity. The second model allows time-varying weather conditions and other practical aspects. Consequently, the resulting strategy are season-dependent. The model is solved using a backward dynamic programming method. The bene ts of the optimal policy are highlighted via a case study that is based upon eld data from the literature and industry. We nd that the optimal policy provides options for cost-e ective actions, because it can be adapted to a variety of operating conditions. Our discrete event-based simulation model incorporates critical components, such as a wind turbine degradation model, power generation model, wind speed model, and maintenance model. We provide practical insights gained by examining di erent maintenance strategies. To the best of our knowledge, our simulation model is the rst discrete-event simulation model for wind farm operations. Last, we present the integration framework, which incorporates the optimization results in the simulation model. Preliminary results reveal that the integrated model has the potential to provide practical guidelines that can reduce the operation costs as well as enhance the marketability of wind energy.

The goal of the study presented in this thesis was the improvement of estimation and monitoring procedures for condition monitoring of belt tension and misalignment in belt-driven automated material handling systems widely used in modern semiconductor manufacturing systems. In pursuit of this goal, two 3-factor, 3-level experiments were designed to study how belt vibration characteristics depend on changes in belt length, belt tension, belt misalignment, and initial location of the excitation of belt vibration. Dependent variables in each of the experiments were drawn from a denoised frequency spectrum calculated from an Autoregressive model of the belt vibration time-series. A feature vector was developed from the Autoregressive features via variance based sensitivity analysis. Results showed that belt vibration characteristics were sensitive to changes in all of the independent variables examined. These results motivated the design of a device to improve the standardized technique widely used to monitor belt tension in belt-driven material handling systems. Reducing variance in the belt length and the location of the initial excitation of belt vibration yielded a reduction of tension estimate standard deviation an order of magnitude, as compared to a human performing the standardized technique. Thus, the use of this device provided higher belt tension estimate resolution. Future work that could lead to a less intrusive technique is presented. / text

In order to stay competitive, it is necessary for companies to continuously increase the effectiveness and efficiency of their production processes. High availability has, thus, increased in importance. Therefore, maintenance has gained in importance as a support function for ensuring, e.g., quality products and on-time deliveries. Maintenance, though, is a costly support function. It has been reported that as much as 70% of the total production cost can be spent on maintenance. Further, as much as one-third of the cost of maintenance is incurred unnecessarily due to bad planning, overtime cost, limited or misused preventive maintenance, and so on. In so, condition based maintenance is introduced as one solution for a more effective maintenance. In condition based maintenance, critical item characteristics are monitored in order to gain early indications of an incipient failure. Research, though, has shown that condition based maintenance has not been implemented on a wide basis. Therefore, the purpose of this research is to investigate how a condition based maintenance approach can be implemented in an industrial setting, and to develop a method that can assist companies in their implementation efforts. Further, the research has been divided in three research questions. They focus on: constituents of a condition based maintenance approach, decision-making prior implementation of condition based maintenance, and finally, the implementation of condition based maintenance in a company. By using a systems approach and a case study process, how condition based maintenance can be implemented as a routine has been investigated. The result is an implementation method in which four suggested phases are presented. The method starts with a feasibility test. It then continues with an analysis phase, an implementation phase, and an assessment phase. The conclusions can be summarized as follows: implementing condition based maintenance consists of many general enabling factors, including management support, education and training, good communication, and motivation etc.

The maintenance cost for an engineering system is an uncertain quantity due to uncertainties associated with occurrence of failure and the time taken to restore the system. The problem of probabilistic analysis of maintenance cost can be modeled as a stochastic renewal-reward process, which is a complex problem. Assuming that the time horizon of the maintenance policy approaches infinity, simple asymptotic formulas have been derived for the failure rate and the cost per unit time. These asymptotic formulas are widely utilized in the reliability literature for the optimization of a maintenance policy. However, in the finite life of highly reliable systems, such as safety systems used in a nuclear plant, the applicability of asymptotic approximations is questionable. Thus, the development of methods for accurate evaluation of expected maintenance cost, failure rate, and availability of engineering systems is the subject matter of this thesis. In this thesis, an accurate derivation of any m-th order statistical moment of maintenance cost is presented. The proposed formulation can be used to derive results for a specific maintenance policy. The cost of condition-based maintenance (CBM) of a system is analyzed in detail, in which the system degradation is modeled as a stochastic gamma process. The CBM model is generalized by considering the random repair time and delay in degradation initiation. Since the expected cost is not informative enough to estimate the financial risk measures, such as Value-at-Risk, the probability distribution of the maintenance cost is derived. This derivation is based on an interesting idea that the characteristic function of the cost can be computed from a renewal-type integral equation, and its Fourier transform leads to the probability distribution. A sequential inspection and replacement strategy is presented for the asset management of a large population of components. The finite-time analyses presented in this thesis can be combined to compute the reliability and availability at the system level. Practical case studies involving the maintenance of the heat transport piping system in a nuclear plant and a breakwater are presented. A general conclusion is that finite time cost analysis should be used for a realistic evaluation and optimization of maintenance policies for critical infrastructure systems.

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

The main focus of this study is to improve the profitability, availability and condition monitoring of Liquefied Natural Gas (LNG) Floating Production Storage and Offloading platforms (FPSOs). Propane pre-cooled, mixed refrigerant (C3MR) liquefaction is the key process in the production of LNG on FPSOs. LNG liquefaction system equipment has the highest failure rates among the other FPSO equipment, and thus the highest maintenance cost. Improvements in the profitability, availability and condition monitoring were made in two ways: firstly, by making recommendations for the use of redundancy in order to improve system reliability (and hence availability); and secondly, by developing an effective condition-monitoring algorithm that can be used as part of a condition-based maintenance system. C3MR liquefaction system reliability modelling was undertaken using the time-dependent Markov approach. Four different system options were studied, with varying degrees of redundancy. The results of the reliability analysis indicated that the introduction of a standby liquefaction system could be the best option for liquefaction plants in terms of reliability, availability and profitability; this is because the annual profits of medium-sized FPSOs (3MTPA) were estimated to increase by approximately US$296 million, rising from about US$1,190 million to US$1,485.98 million, if redundancy were implemented. The cost-benefit analysis results were based on the average LNG prices (US$500/ton) in 2013 and 2014. Typically, centrifugal turbines, compressors and blowers are the main items of equipment in LNG liquefaction plants. Because centrifugal equipment tops the FPSO equipment failure list, a Condition Monitoring (CM) system for such equipment was proposed and tested to reduce maintenance and shutdown costs, and also to reduce flaring. The proposed CM system was based on a novel FFT-based segmentation, feature selection and fault identification algorithm. A 20 HP industrial air compressor system with a rotational speed of 15,650 RPM was utilised to experimentally emulate five different typical centrifugal equipment machine conditions in the laboratory; this involved training and testing the proposed algorithm with a total of 105 datasets. The fault diagnosis performance of the algorithm was compared with other methods, namely standard FFT classifiers and Neural Network. A sensitivity analysis was performed in order to determine the effect of the time length and position of the signals on the diagnostic performance of the proposed fault identification algorithm. The algorithm was also checked for its ability to identify machine degradation using datasets for which the algorithm was not trained. Moreover, a characterisation table that prioritises the different fault detection techniques and signal features for the diagnosis of centrifugal equipment faults, was introduced to determine the best fault identification technique and signal feature. The results suggested that the proposed automated feature selection and fault identification algorithm is effective and competitive as it yielded a fault identification performance of 100% in 3.5 seconds only in comparison to 57.2 seconds for NN. The sensitivity analysis showed that the algorithm is robust as its fault identification performance was affected by neither the time length nor the position of signals. The characterisation study demonstrated the effectiveness of the AE spectral feature technique over the fault identification techniques and signal features tested in the course of diagnosing centrifugal equipment faults. Moreover, the algorithm performed well in the identification of machine degradation. In summary, the results of this study indicate that the proposed two-pronged approach has the potential to yield a highly reliable LNG liquefaction system with significantly improved availability and profitability profiles.

The growth of global competition has led to remarkable changes in the way manufacturing companies operate. These changes have affected maintenance and made its role even more crucial for business success. To remain competitive, manufacturing companies must continuously increase the effectiveness and efficiency of their production processes. Furthermore, the introduction of lean manufacturing has increased concerns regarding equipment availability and, therefore, the demand for effective maintenance. That maintenance is becoming more important for the manufacturing industry is evident in current discussions on national industrialization agendas. Digitalization, the industrial internet of things (IoT) and their connections to sustainable production are identified as key enablers for increasing the number of jobs in industry. Agendas such as “Industry 4.0” in Germany and “Smart Industry” in Sweden are promoting the connection of physical items such as sensors, devices and enterprise assets, both to each other and to the internet. Machines, systems, manufactured parts and humans will be closely interlinked to collaborative actions. Every physical object will formulate a cyber-physical system (CPS), and it will constantly be linked to its digital fingerprint and to intensive connection with the surrounding CPSs of its on-going processes. That said, despite the increasing demand for reliable production equipment, few manufacturing companies pursue the development of strategic maintenance. Moreover, traditional maintenance strategies, such as corrective maintenance, are no longer sufficient to satisfy industrial needs, such as reducing failures and degradations of manufacturing systems to the greatest possible extent. The concept of maintenance has evolved over the last few decades from a corrective approach (maintenance actions after a failure) to a preventive approach (maintenance actions to prevent the failure). Strategies and concepts such as condition based maintenance (CBM) have thus evolved to support this ideal outcome. CBM is a set of maintenance actions based on the real-time or near real-time assessment of equipment conditions, which is obtained from embedded sensors and/or external tests and measurements, taken by portable equipment and/or subjective condition monitoring. CBM is increasingly recognized as the most efficient strategy for performing maintenance in a wide variety of industries. However, the practical implementation of advanced maintenance technologies, such as CBM, is relatively limited in the manufacturing industry. Based on the discussion above, the objective of this research is to provide frameworks and guidelines to support the development and implementation of condition based maintenance in manufacturing companies.  This thesis will begin with an overall analysis of maintenance management to identify factors needed to strategically manage production maintenance. It will continue with a focus on CBM to illustrate how CBM could be valued in manufacturing companies and what the influencing factors to implement CBM are. The data were collected through case studies, mainly at one major automotive manufacturing site in Sweden. The bulk of the data was collected during a pilot CBM implementation project. Following the findings from these efforts, a formulated maintenance strategy is developed and presented, and factors to evaluate CBM cost effectiveness are assessed. These factors indicate the benefits of CBM, mostly with regard to reducing the probability of experiencing maximal damage to production equipment and reducing production losses, particularly at high production volumes. Furthermore, a process of CBM implementation is presented. Some of the main elements in the process are the selection of the components to be monitored, the techniques and technologies for condition monitoring and their installation and, finally, the analysis of the results of condition monitoring. Furthermore, CBM of machine tools is presented and discussed in this thesis, focusing on the use of vibration monitoring technique to monitor the condition of machine tool spindle units. / INNOFACTURE - innovative manufacturing development