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- Al-Kahwati, Kammal, et al.
(author)
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Condition Monitoring of Rollers in Belt Conveyor Systems
- 2021
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In: 2021 5th International Conference on Control and Fault-Tolerant Systems (SysTol). - : IEEE.
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Conference paper (peer-reviewed)abstract
- Predictive maintenance strategies for the mining sector are of utmost importance considering the automated behavior of industrial systems and the oftentimes inaccessible environment around belt conveyor systems. In this paper, we present a model combining IoT sensors and dead-reckoning modeling, focused on early theoretical work in the field of modeling the behavior of belt conveyor systems to act as a decision support tool in maintenance strategies, by estimating the remaining useful life (RUL) of rotating components in a belt conveyor system. The estimation of RUL is a function of the degradation of the ball bearings in idler rollers due to the forces acting on the rollers during the conveyance of material. The forces occur due to the material loading, the belt weight, roller shell weight, and the idler misalignment load (IML). Furthermore, the dynamics of bulk material during conveyance can be modeled in several ways considering earth pressure theory. A model considering this is derived from the Krausse Hettler method to determine the forces acting on the wing rollers of a thee-roll idler trough set by the notion that the bulk material undergoes active and passive stress states during conveyance. The model is further compared and extended to the works of Sokolovski, to get a bounded delta RUL reduction estimate on the roller bearings in each idler set of a belt conveyor system.
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| 2. |
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| 3. |
- Saari, Esi
(author)
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KPI framework for maintenance management through eMaintenance : Development, implementation, assessment, and optimization
- 2019
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Doctoral thesis (other academic/artistic)abstract
- Performance measurement is critical if any organization wants to thrive. The motivation for the thesis originated from the project “Key Performance Indicators (KPI) for control and management of maintenance process through eMaintenance (in Swedish: Nyckeltal för styrning och uppföljning av underhållsverksamhet m h a eUnderhåll)”, initiated and financed by a mining company in Sweden. The main purpose of this project is to propose an integrated KPI system for the mining company’s maintenance process through eMaintenance, including development, implementation, assessment, and optimization.There are gaps in the research, however, resulting in the following challenges. First, no KPI framework considers both technical and soft KPIs, so developing a system is problematic. Second, few studies have focused on implementing KPI measurement through eMaintenance. Third, there are gaps in KPI assessment. In assessing system availability, for example, the current analytical (e.g., Markov/semi-Markov) or simulation approaches (e.g., Monte Carlo simulation-based) cannot handle complicated state changes or are computationally expensive. In addition, few researchers have revealed the connections between technical and soft KPIs. For those soft KPIs for which the distribution of data collected from eMaintenance systems (e.g., work orders) is not easily determined, studies are insufficient. Fourth, the current continuous improvement process for the KPIs is very time-consuming. In short, there is a need for a new approach.The thesis develops an integrated KPI framework consisting of technical KPIs (linked to machines) and soft KPIs (linked to maintenance workflow) to control and monitor the entire maintenance process to achieve the overall goals of the organization. The proposed KPI framework makes use of four hierarchical levels and has 134 KPIs divided into technical and soft KPIs as follows: asset operation management has 23 technical KPIs, maintenance process management has 85 soft KPIs and maintenance resources management has 26 soft KPIs.The thesis discusses the proposed KPI framework; it lists the KPIs and provides timelines, definitions and general formulas for each specified KPI. Results will be used by the mining company to guide the implementation of the proposed KPIs in an eMaintenance environment.To suggest novel approaches to KPI assessment, the thesis takes system availability in the operational stage as an example. It proposes parametric Bayesian approaches to assess system availability. With these approaches, Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR) can be treated as distributions instead of being “averaged” by point estimation. This better reflects reality. Markov Chain Monte Carlo (MCMC) approach is adopted to take advantage of both analytical and simulation methods. Because of MCMC’s high dimensional numerical integral calculation, the selection of prior information and descriptions of reliability/maintainability can be more flexible and realistic. The limitations of data sample size can also be compensated for. In the case studies, Time to Failure (TTF) and Time to Repair (TTR) are determined using a Bayesian Weibull model and a Bayesian lognormal model, respectively. The proposed approach can integrate analytical and simulation methods for system availability assessment and could be applied to other technical problems in asset management (e.g., other industries, other systems). By comparing the results with and without considering the threshold for censoring data, the research shows the connection between technical and soft KPIs, and suggests the threshold can be used as a monitoring line for continuous improvement in the mining company. For those soft KPIs for which the distribution of data collected from the eMaintenance system (e.g., work orders) is not easily determined, other approaches, such as time series analysis (if the data are “fast moving”), the Croston method (if the data are “intermittent”), or the bootstrap method (if the data are “slow moving”) could be applied. To ensure the KPI framework can be improved continuously, the thesis performs a comparison study to find the gaps between current and proposed KPIs in the mining company. It adapts a roadmap from the railway industry to show how optimization can be promoted by reviewing and improving the KPI framework.Results from this study will be applied to the company and guide its development, implementation and assessment of the KPIs through eMaintenance with continuous improvement. The proposed approaches could also be applied to other technical problems in asset management (e.g., other industries, other system).
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| 4. |
- Saari, Esi, et al.
(author)
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Novel Bayesian Approach to Assess System Availability using a Threshold to Censor Data
- 2019
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In: International Journal of Performability Engineering. - : Totem Publisher, Inc.. - 0973-1318. ; 15:5, s. 1314-1325
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Journal article (peer-reviewed)abstract
- Assessment of system availability has been studied from the design stage to the operational stage in various system configurations using either analytic or simulation techniques. However, the former cannot handle complicated state changes, and the latter is computationally expensive. This study proposes a Bayesian approach to evaluate system availability. In this approach: 1) Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR) are treated as distributions instead of being "averaged" to better describe real scenarios and overcome the limitations of data sample size; 2) Markov Chain Monte Carlo (MCMC) simulations are applied to take advantage of the analytical and simulation methods; and 3) a threshold is set up for Time to Failure (TTR) data and Time to Repair (TTR) data, and new datasets with right-censored data are created to reveal the connections between technical and "Soft" KPIs. To demonstrate the approach, the paper considers a case study of a balling drum system in a mining company. In this system, MTTF and MTTR are determined by a Bayesian Weibull model and a Bayesian lognormal model, respectively. The results show that the proposed approach can integrate the analytical and simulation methods to assess system availability and could be applied to other technical problems in asset management (e.g., other industries, other systems). By comparing the results with and without considering the threshold for censoring data, we show the threshold can be used as a monitoring line for continuous improvement in the investigated mining company.
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| 5. |
- Saari, Esi, et al.
(author)
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System availability assessment using a parametric Bayesian approach : a case study of balling drums
- 2019
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In: International Journal of Systems Assurance Engineering and Management. - : Springer. - 0975-6809 .- 0976-4348. ; 10:4, s. 739-745
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Journal article (peer-reviewed)abstract
- Assessment of system availability usually uses either an analytical (e.g., Markov/semi-Markov) or a simulation approach (e.g., Monte Carlo simulation-based). However, the former cannot handle complicated state changes and the latter is computationally expensive. Traditional Bayesian approaches may solve these problems; however, because of their computational difficulties, they are not widely applied. The recent proliferation of Markov Chain Monte Carlo (MCMC) approaches have led to the use of the Bayesian inference in a wide variety of fields. This study proposes a new approach to system availability assessment: a parametric Bayesian approach using MCMC, an approach that takes advantages of the analytical and simulation methods. By using this approach, mean time to failure (MTTF) and mean time to repair (MTTR) are treated as distributions instead of being “averaged”, which better reflects reality and compensates for the limitations of simulation data sample size. To demonstrate the approach, the paper considers a case study of a balling drum system in a mining company. In this system, MTTF and MTTR are determined in a Bayesian Weibull model and a Bayesian lognormal model respectively. The results show that the proposed approach can integrate the analytical and simulation methods to assess system availability and could be applied to other technical problems in asset management (e.g., other industries, other systems).
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