Cost optimization of maintenance scheduling for wind turbines with aging components

A major part of the wind turbine operation cost is resulted from the maintenance of its components. This thesis deals with the theory, algorithms, and applications concerning minimization of the maintenance cost of wind power turbines, using mathematical modelling to find the optimal schedules of preventive maintenance activities for multi-component systems.\ua0 \ua0 The main contributions of this thesis are covered by the four papers appended. The unifying goal of these papers is to produce new optimization models resulting in effective and fast algorithms for preventive maintenance time schedules. The features of the multi-component systems addressed in our project are: aging components, long-term, and short-term planning, planning for a wind power farm, end of the lifetime of the wind farm, maintenance contracts, and condition monitoring data.\ua0 \ua0 For the long-term maintenance planning problem, this thesis contains an optimization framework that recognizes different phases of the wind turbine lifetime. For short-term planning problem, this thesis contains two modeling frameworks, which both focus on the planning of the next preventive maintenance activities. Our virtual experiments show that the developed optimization models adopt realistic assumptions and can be accurately solved in seconds. One of these two frameworks is further extended so that available condition monitoring data can be incorporated for regular updates of the components\u27 hazard functions. In collaboration with the Swedish Wind Power Technology Center at Chalmers and its member companies, we test this method with real-world wind farm data. Our case studies demonstrate that this framework may result in remarkable savings due to the smart scheduling of preventive maintenance activities by monitoring the ages of the components as well as operation data of the wind turbines. \ua0 \ua0 We believe that in the future, the proposed optimization model for short-term planning based on the component age and condition monitoring data can be used as a key module in a maintenance scheduling app

Monitoring of Critical Metallic Assets in Oil and Gas Industry Using Ultrasonic Guided Waves

This chapter presents advancements in structural health monitoring (SHM) using ultrasonic guided waves (UGW) technology for metallic structures to support their integrity and maintenance management. The focus is on pipelines and storage tanks, which are critical assets in the Oil and Gas industry, whose operational conditions can greatly accelerate damage mechanisms. Conventional routine inspections are both costly and time consuming and affect the plant reliability and availability. These operational and economic disadvantages have led to development of SHM systems which can be permanently installed on these critical structures to provide information about developing damage and optimise maintenance planning and ensure structural integrity. These technology advancements enable inspection without interruption to operations, and generate diagnosis and prognosis data for condition-based maintenance, hence increasing safety and operational efficiency. The fundamentals, architecture and development of such SHM systems for pipes and above ground storage tanks are described here

Multi-signal Accelerated Degradation Testing of Rolling Ball Bearings Through Radial Overload

Bearings are essential components in rotating machinery found in abundance in nuclear power plants. Bearing failure in nuclear power plants can lead to increased operations and maintenance costs and even plant trips. When developing maintenance procedures, it is ideal to minimize costs and equipment downtime while maximizing safety. Reactive, or run-to-failure, maintenance minimizes maintenance costs at the expense of operation costs and safety. Preventative, or time-based, maintenance maximizes safety and minimizes operation costs at the expense of equipment downtime and maintenance costs. Predictive, or condition-based, maintenance attempts to optimize overall costs while maintaining system safety and reducing downtown. Predictive maintenance uses online equipment condition assessment and remaining useful life (RUL) predictions to schedule inspection and maintenance actions. The development of methods for early and accurate RUL predictions for bearings has the potential to transform maintenance planning in the nuclear power industry, reducing operation and maintenance costs while maintaining or improving overall system safety, reliability, and economics. In order to develop robust RUL models, examples of run-to-failure data are needed. Using data collected during accelerated degradation tests has the advantages of being easily controlled and of providing ample data over relatively a short test period. A testbed has been designed and constructed that incites bearing failure through the application of a radial load. Several parameters are monitored continuously and online, including motor current, shaft rotational speed, acoustics and bearing vibration and temperature. Bearing maintenance in nuclear power plants to date has relied on vibration data analysis performed at defined inspection intervals. By including several process signals in the testbed design, recommendations are made for online monitoring of bearings in nuclear power plants that would augment, or perhaps replace, the current maintenance scheme with gains in safety, economics, and system reliability

Employee Perception of Maintenance Practices at Selected Public Healthcare Facilities in Niger State, Nigeria

Maintenance practice involves deliberate and orderly way that deals with planning, evaluation, organizing, and monitoring of maintenance activities and their expenses. An excellent maintenance management framework combines with learned and proficient maintenance staff can avoid safety and health issues and environmental harm; yielding longer assets life with less breakdowns, lower working costs and higher personal satisfaction for the users and occupants. Experienced and highly trained workers are inspired with a very friendly atmosphere and they are also in turn individual friendly.Lack of maintenance of our healthcare facilities is evident in the deplorable condition of the structures and equipment.This study assessed maintenance practices of maintenance staff at six (6) selected healthcare facilities in Niger State through a structured questionnaire. Data collected was analysed with Minitab 17 statistical software using descriptive statistics. The analysis revealed among others that majority of maintenance practice were preventive in nature, and that the maintenanceproblems in the healthcare facilities of Niger State was caused bylack of lack of funding and lack of successful adaptation of ineffective maintenance programmes and practices. The study recommended a proactive and aggressive approach to reduce the occurrence of defects in and around the healthcare facilities. It was also recommended that individual healthcare centres should solicit for both private and public funding for maintenance activities since they have partial autonomy to generate revenue internally for their operation

Faktor faktor yang mempengaruhi penyelenggaraan jalanraya tidak diselengggara dengan baik oleh pihak berkuasa tempatan

Roads maintenance management are an important role to provide comfort and improve service quality Local Authorities (LA). It requires a systematic mechanism and to ensure the proper planning of roads are in good condition and provide comfort to the motorist. However, maintenance work under the supervision of local authorities still at an unsatisfactory level and cause problems for road users. Therefore, this study aimed to identify factors that lead to the failure of maintenance work under the supervision of local authorities were poorly maintained. Therefore, this study aimed to identify the cause of the roads under the Local Authorities were poorly maintained. The data obtained is through a literature review and a total of 52 respondents from local authority’s staff who involved in monitoring of the road maintenance has been chosen randomly. The questionnaires used Likert Scale testing is involved. This questionnaire involves four components. Data were analyzed by using frequency analysis methods. According to this study, four factors to be given due attention in terms of planning, monitoring, control and training. The results of this study will be beneficial to the local authorities to identify the cause of the failure maintenance work. This study can be used to improve the quality of road maintenance work

Highway filter drains maintenance management

Across a large part of the UK highways network the carriageway and pavement foundations are drained by Highway Filter Drains (HFDs). A HFD is a linear trench constructed either at the pavement edge or central reserve, fitted with a porous carrier pipe at the base and backfilled with an initially highly porous aggregate material. This arrangement enables the swift removal of surface runoff and subsurface water from the pavement system minimising road user hazards and eliminating risk of structural damage to the pavement sub-base. The highly porous backfill filters throughout its operational life fines washed from the pavement wearing course or adjacent land. HFDs have been found to be prone to collecting near the basal sections (pipe) or surface layers contaminants or detritus that causes the filter media to gradually block. The process has been defined as HFD clogging and it has been found to lead to reduced drainage capacity and potentially severe drop of serviceability. O&M contractual agreements for DBFO projects usually propose in-service and handback requirements for all assets included in the concession portfolio. Different performance thresholds are thus prescribed for pavements, structures, ancillary assets or street lighting. Similar definitions can be retrieved for drainage assets in such agreements, and these include HFDs. Performance metrics are defined though in a generic language and residual life (a key indicator for major assets that usually drives long-term maintenance planning) is prescribed without indicative means to evaluate such a parameter. Most of pavement maintenance is carried out nowadays using proactive management thinking and engineered assessment of benefits and costs of alternative strategies (what-if scenarios). Such a proactive regime is founded upon data driven processes and asset specific ageing / renewal understanding. Within the spectrum of road management, maintenance Life Cycle Costs are usually generated and updated on an annual basis using inventory and condition data linked to a Decision Support Tool (DST). This enables the assessment and optimisation of investment requirements and projection of deterioration and of treatment impacts aligned to continuous monitoring of asset performance. Following this paradigm shift in infrastructure management, a similar structured methodology to optimise HFD maintenance planning is desired and is introduced in this thesis. The work presented enables the identification of proactive maintenance drivers and potential routes in applying a systemised HFD appraisal and monitoring system. An evaluation of Asset Management prerequisites is thus discussed linked to an overview of strategic requirements to establish such a proactive approach. The thesis identifies condition assessment protocols and focuses on developing the means to evaluate deteriorated characteristics of in service drains using destructive and non-destructive techniques. A probabilistic HFD ageing / renewal model is also proposed using Markov chains. This builds upon existing deterioration understanding and links back to current treatment options and impacts. A filter drain decision support toolkit is lastly developed to support maintenance planning and strategy generation

Monitoring Street Infrastructures with Artificial Intelligence

Sensor-based IoT data is enhancing information gathering methods for urban planning in many ways and, due to the growing data pool provided by these sensors, more and more cities and municipalities are consequently putting the use of artificial intelligence-based (AI) methods on their agenda. One area of urban planning that will benefit significantly from the new possibilities enabled by AI is that of infrastructure monitoring. As the topic of the investment backlog of German road infrastructures increasingly pushes into public discourse, many potential areas for application of such a system are opening up. Given the fact that a large part of the German road infrastructure was planned and built several decades ago, and considering that the traffic volume has increased tremendously since then, the urgency in the development of improved maintenance methods is evident: Today's solutions for infrastructure monitoring are either too labor-intensive, too resource-intensive or too inflexible for the scenario at hand. However, a promising avenue for further research opened up through the advent of mobile communication devices, such as smartphones, incombination with artificial intelligence approaches. This paper describes the methodology applied in the ongoing research project DatEnKoSt, in which these comparatively cheap and sensor-laden devices are used to realize low-cost acquisition methods: Mounting the smartphone in a vehicle, a multi-sensor datastream can be recorded, including, for instance, accelerometer data, GPS coordinates, image or even audio data. From the datastream, features correlated with the road condition can then be extracted, e.g., image processing methods may extract individual cracks from the image data, signal processing can aid analysis of the accelerometer data to determine strength of vibrations, etc.. Using supervised learning methods, thesefeatures may be mapped to standardized profiles of the current state of the infrastructure. Even more,predictive methods can, in addition to a mere monitoring of the current state of the infrastructure, enable new ways to provide more precise forecasts and eventually, leveraging optimization algorithms, automatically derive the right maintenance measures for each given situation. The municipal preservation of traffic routes becomes more efficient and sustainable. The methodology enables the potential for further use in the light of real-time as well as predictive road infrastructure monitoring such as winter road services

Smart Asset Management for Electric Utilities: Big Data and Future

This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises "How to extract information from large chunk of data?" The concept of "rich data and poor information" is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on Engineering Asset Management (WCEAM) 201