Katarzyna Antosz

Machinery classification and prioritization: Empirical models and AHP based approach for effective preventive maintenance

Empirical models play an important role in the classification of machinery for scheduling preventive maintenance (PM) activities for machinery in manufacturing firms (MFs). First, this manuscript discusses the development of empirical models for classifying machines in an MF and the structure of each empirical model. Then, it presents the classification of machinery using the developed empirical models in two different manufacturing areas. It then goes on to demonstrate the development of a common model for the two different manufacturing areas in the case study MF. It is a challenge to allocate the available resources, when the classified groups contain a large amount of machinery. Hence, machines have been grouped into families or types based on their similarities, and an analytic hierarchy process (AHP) has been employed to further prioritize each type of machinery. A generic hierarchical model has been developed to prioritize machinery in the case study MF. Then, the use of AHP has been demonstrated using a tailor-made hierarchical model based on a selected type of machinery, focusing on prioritizing the necessary PM activities.

Deriving an empirical model for machinery prioritization: Mechanical systems maintenance

The maintenance of mechanical systems plays a significant role in production and manufacturing settings. In this context, prioritization of machinery is vital for minimizing the economic burden due to higher maintenance cost, loss of production, potential damage to health, safety and environment. Different production and/or manufacturing firms use different approaches to mitigate the aforementioned. These approaches are tailor-made to fit a selected production or manufacturing operation and the firms requirements, creating a difficulty in generalizing their utilization in a generic application. Hence, it is vital to analyze the existing models that have been used for the machinery prioritization and derive a generic model, enabling it to be utilized in a generic application. This manuscript illustrates a study carried out in three firms which utilize machinery for their production and/or manufacturing activities. The empirical models that have been employed for machinery prioritization by the aforementioned firms were analyzed and a generic empirical model has been derived. The empirical model has been validated by a randomly selected firm and one item of its machinery.

Forecasting the Mountability Level of a Robotized Assembly Station

This article presents the problem of determining the mountability level of the assembly station using an artificial neural network (ANN). The results of ANN modelling were compared with the results of experimental research and classical mathematical modelling. It was found that the error in predicting the mountability level using the artificial neural network is about two-fold lower than in the case of the error determined by classical mathematical modelling. Although the neural network ensures a lower prediction error, to obtain a good prediction it is necessary to conduct many experiments in the whole workspace of the robots to build a training set. Despite the worst prediction, a mathematical model of the mountability level only requires an analytical description of the kinematic structure of the assembly robot, so in industrial applications this is preferred due to the lower labour requirement.

Overall Equipment Effectiveness: Analysis of Different Ways of Calculations and Improvements

An Overall Equipment Effectiveness (OEE) indicator is one of the most often used indicators, especially in large companies, to assess the level of the successful utilization of equipment. Many companies still do not calculate OEE, although the procedure of the OEE calculation is well known. On the basis of the literature review and from companies practice, it occurs that different methods of the OEE assessment are used. It is because companies identify different disturbances which influence the OEE value. The authors summarized these disturbances. In the paper, two case studies concerning the OEE calculations from manufacturing companies are presented. On the basis of the first company, it is shown how the Total Productive Maintenance (TPM) implementation may improve the value of OEE. On the base of the other company, the way the OEE calculation procedure can be improved and the results of the calculation procedure modification are presented. Finally, the authors propose to introduce weights to differentiate the influence of different OEE components. The results of the new procedure implementation are presented in the paper.

An Intelligent System Supporting a Maintenance Process of Specialised Medical Equipment

A maintenance process of medical equipment is very important because people’s life or the quality of people’s life depend on such equipment. The maintenance of some equipment can be only done by specialists. Nevertheless, some actions can be undertaken by the less specialized personnel. In this paper, on the basis of the data concerning complaints, the kind of failures and exchanged parts in equipment, preventive actions were proposed. However, the actions are recommended not to prevent failures but to avoid a long waiting time for the equipment to be repaired. The authors propose to use a fuzzy logic analysis to assess the risk of failures of a certain piece of equipment. The defects which can be eliminated by the less specialized personnel were indicated. The results of the analysis are taken into consideration in a decision making process identifying the situations in which a certain person, i.e. a dealer, should be trained to be able to make a repair without sending the equipment to a manufacturer.

An Intelligent System Supporting a Forklifts Maintenance Process

A maintenance process of forklifts presented in this work is realized by a service organization which delivers services for several companies that use forklifts. In the work, the fuzzy logic was implemented to assess the risk of failures for different groups of forklifts. The results of the analysis are to be taken into consideration by the service company in the decision making process. The plan of maintenance activities for each client’s forklifts can be developed and adequate maintenance activities can be indicated for each group on the basis of the risk of failures.

Safena and QBPM: a proposition for modeling and enacting processes in supply chain network

The heterogeneity of process modeling languages is a problem of BPM that needs to be solved. Business process modeling notation typically addresses the process design stage. There are also process enactment environments like BPEL. The graphical modeling BPMN standard can be easily used by nontechnical analysts while execution standards like BPEL can be hardly understood by non-programmers. There is a lack of interchangeable standards covering all of the functionalities needed by BPM analyst. Although there are many attempts to fill all the gaps in business process modeling environment, no successful proposition has been made. There have been many attempts to create bi-directional mapping between the two languages. The QBPM is uniform design and execution environment which addresses this problem. It enables execution of native BPMN diagrams created with open standards edition tools such as open source Eclipse BPMN implementation.