Maintenance management of tractors and agricultural machinery: Preventive maintenance systems

Agricultural machinery maintenance has a crucial role for successful agricultural production.  It aims at guaranteeing the safety of operations and availability of machines and related equipment for cultivation operation.  Moreover, it is one major cost for agriculture operations.  Thus, the increased competition in agricultural production demands maintenance improvement, aiming at the reduction of maintenance expenditures while keeping the safety of operations.  This issue is addressed by the methodology presented in this paper.  So, the aim of this paper was to give brief introduction to various preventive maintenance systems specially condition-based maintenance (CBM) techniques, selection of condition monitoring techniques and understanding of condition monitoring (CM) intervals, advancement in CBM, standardization of CBM system, CBM approach on agricultural machinery, advantages and disadvantages of CBM.  The first step of the methodology consists of concept condition monitoring approach for the equipment preventive maintenance; its purpose is the identification of state-of-the-art in the CM of agricultural machinery, describing the different maintenance strategies, CM techniques and methods.  The second step builds the signal processing procedure for extracting information relevant to targeted failure modes.   Keywords: agricultural machinery, fault detection, fault diagnosis, signal processing, maintenance managemen

A transition from manual to Intelligent Automated power system operation -A Indicative Review

This paper reviews the transition of the power system operation from the traditional manual mode of power system operations to the level where automation using Internet of Things (IOT) and intelligence using Artificial Intelligence (AI) is implemented. To make the review paper brief only indicative papers are chosen to cover multiple power system operation based implementation. Care is taken there is lesser repeatation of similar technology or application be reviewed. The indicative review is to take only a representative literature to bypass scrutinizing multiple literatures with similar objectives and methods. A brief review of the slow transition from the traditional to the intelligent automated way of carrying out power system operations like the energy audit, load forecasting, fault detection, power quality control, smart grid technology, islanding detection, energy management etc is discussed .The Mechanical Engineering Perspective on the basis of applications would be noticed in the paper although the energy management and power delivery concepts are electrical

Strategic maintenance technique selection using combined quality function deployment, the analytic hierarchy process and the benefit of doubt approach

The business performance of manufacturing organizations depends on the reliability and productivity of equipment, machineries and entire manufacturing system. Therefore, the main role of maintenance and production managers is to keep manufacturing system always up by adopting most appropriate maintenance methods. There are alternative maintenance techniques for each machine, the selection of which depend on multiple factors. The contemporary approaches to maintenance technique selection emphasize on operational needs and economic factors only. As the reliability of production systems is the strategic intent of manufacturing organizations, maintenance technique selection must consider strategic factors of the concerned organization along with operational and economic criteria. The main aim of this research is to develop a method for selecting the most appropriate maintenance technique for manufacturing industry with the consideration of strategic, planning and operational criteria through involvement of relevant stakeholders. The proposed method combines quality function deployment (QFD), the analytic hierarchy process (AHP) and the benefit of doubt (BoD) approach. QFD links strategic intents of the organizations with the planning and operational needs, the AHP helps in prioritizing the criteria for selection and ranking the alternative maintenance techniques, and the BoD approach facilitates analysing robustness of the method through sensitivity analysis through setting the realistic limits for decision making. The proposed method has been applied to maintenance technique selection problems of three productive systems of a gear manufacturing organization in India to demonstrate its effectiveness

Intelligent Systems Supporting the Use of Energy Systems and Other Complex Technical Objects, Modeling, Testing and Analysis of Their Reliability in the Operation Process

The book focuses on a novel application of Intelligent Systems for supporting the operation and maintenance of power systems or other technical facilities within wind farms. Indicating a different perception of the reliability of wind farm facilities led to the possibility of extending the operation lifetime and operational readiness of wind farm equipment. Additionally, the presented approach provides a basis for extending its application to the testing and analysis of other technical facilities

Case Study - Bulgaria, Sustainable Agriculture and Soil Conservation (SoCo Project)

This Technical Note 'Case Study ¿ Bulgaria' is part of a series of case studies within the ¿Sustainable Agriculture and Soil Conservation¿ (SoCo) project. Ten case studies were carried out in Belgium, Bul-garia, the Czech Republic, Denmark, France, Germany, Greece, Italy, Spain and the United Kingdom between spring and summer 2008. The selection of case study areas was designed to capture differences in soil degradation processes, soil types, climatic conditions, farm structures and farming prac-tices, institutional settings and policy priorities. A harmonised methodological approach was pursued in order to gather insights from a range of contrasting conditions over a geographically diverse area. The case studies were carried out by local experts to reflect the specificities of the selected case studies.JRC.DDG.J.5-Agriculture and Life Sciences in the Econom

Intelligent Data Fusion for Applied Decision Support

Data fusion technologies are widely applied to support a real-time decision-making in complicated, dynamically changing environments. Due to the complexity in the problem domain, artificial intelligent algorithms, such as Bayesian inference and particle swarm optimization, are employed to make the decision support system more adaptive and cognitive. This dissertation proposes a new data fusion model with an intelligent mechanism adding decision feedback to the system in real-time, and implements this intelligent data fusion model in two real-world applications. The first application is designing a new sensor management system for a real-world and highly dynamic air traffic control problem. The main objective of sensor management is to schedule discrete-time, two-way communications between sensors and transponder-equipped aircraft over a given coverage area. Decisions regarding allocation of sensor resources are made to improve the efficiency of sensors and communications, simultaneously. For the proposed design, its loop nature takes account the effect of the current sensor model into the next scheduling interval, which makes the sensor management system able to respond to the dynamically changing environment in real-time. Moreover, it uses a Bayesian network as the mission manager to come up with operating requirements for each region every scheduling interval, so that the system efficiently balances the allocation of sensor resources according to different region priorities. As one of this dissertation\u27s contribution in the area of Bayesian inference, the resulting Bayesian mission manager is shown to demonstrate significant performance improvement in resource usage for prioritized regions such as a runway in the air traffic control application for airport surfaces. Due to wind\u27s importance as a renewable energy resource, the second application is designing an intelligent data-driven approach to monitor the wind turbine performance in real-time by fusing multiple types of maintenance tests, and detect the turbine failures by tracking the turbine maintenance statistics. The current focus has been on building wind farms without much effort towards the optimization of wind farm management. Also, under performing or faulty turbines cause huge losses in revenue as the existing wind farms age. Automated monitoring for maintenance and optimizing of wind farm operations will be a key element in the transition of wind power from an alternative energy form to a primary form. Early detection and prediction of catastrophic failures helps prevent major maintenance costs from occurring as well. I develop multiple tests on several important turbine performance variables, such as generated power, rotor speed, pitch angle, and wind speed difference. Wind speed differences are particularly effective in the detection of anemometer failures, which is a very common maintenance issue that greatly impacts power production yet can produce misleading symptoms. To improve the detection accuracy of this wind speed difference test, I discuss a new method to determine the decision boundary between the normal and abnormal states using a particle swarm optimization (PSO) algorithm. All the test results are fused to reach a final conclusion, which describes the turbine working status at the current time. Then, Bayesian inference is applied to identify potential failures with a percentage certainty by monitoring the abnormal status changes. This approach is adaptable to each turbine automatically, and is advantageous in its data-driven nature to monitor a large wind farm. This approach\u27s results have verified the effectiveness of detecting turbine failures early, especially for anemometer failures

Bioenergy Entrepreneurship in Rural China

The Chinese Central Government has made the promotion of industrial utilization of bioenergy one of the priorities in the national plans on renewable energy. However, there is a general lack of understanding on how bioenergy entrepreneurship works on the ground level in China, particularly in rural areas. This thesis therefore aims to investigate the emergence and development of rural bioenergy business in China, and to identify and evaluate the key factors and enabling conditions for bioenergy systems. The research is based on three empirical case studies and interviews with key stakeholders; it finds that the individual innovation from primary movers can become a major motivation to initiate rural bioenergy business, and this innovation is derived from primary movers ambitious and personal value judgment. During the system development, local networks play a critical role to formulate business strategies. One of the key market advantages of rural bioenergy business is the naturally ready local networks in rural China, given the inherent close relationship inside the village system, which works effectively to activate relevant actors and integrate resources. Given these attributes, the internal primary movers are more likely to bring positive performance than external primary movers. A joint force of internal and external actors is also promising in terms of performance, when the internal actors take the leading role. The research highlights three conceptualized development mechanisms of rural bioenergy that have been derived from the empirical cases. They include environmental oriented development, social welfare oriented development and entrepreneurial oriented development. The three mechanisms start with different entrepreneurs orientations with the focus on environmental protection, social welfare improvement and innovative business development, and bring out different system outcomes based on value-creation consideration. The enabling solutions for the development of rural bioenergy business therefore require comprehensive institutional improvement, including enhanced supportive policies, and various education, information sharing facilitations

Information Application for Multicriterial Optimum

The management activity does not only include the techniques and methods of programming, organizing and allocation of resources, starting and control of operations, interventions but it also implies a great number of decisions regarding the launching, carrying on, modifying and carrying out of activities or choosing one of the possible variants so as to ensure that the goals should be reached. The activity of choosing one variant from several possible ones is often met with in maintenance management, such as: the selection of an optimum equipment, the choice of a firm for after/sale service, for supplying materials or spare parts which implies taking into account a large number of factors. The choice based on fundamental mathematic methods becomes feasible by using the current automatic data processing devices and this paper presents the “Xomc” application of establishing the multicriterial optimum.maintenance management, multicriterial optimum, fundamental mathematic methods