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Machinery Prognostics and Prognosis Oriented Maintenance Management von Yan, Jihong (eBook)

  • Erscheinungsdatum: 10.11.2014
  • Verlag: Wiley
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Machinery Prognostics and Prognosis Oriented Maintenance Management

This book gives a complete presentatin of the basic essentials of machinery prognostics and prognosis oriented maintenance management, and takes a look at the cutting-edge discipline of intelligent failure prognosis technologies for condition-based maintenance. Latest research results and application methods are introduced for signal processing, reliability moelling, deterioration evaluation, residual life prediction and maintenance-optimization as well as applications of these methods.


    Format: ePUB
    Kopierschutz: AdobeDRM
    Seitenzahl: 375
    Erscheinungsdatum: 10.11.2014
    Sprache: Englisch
    ISBN: 9781118638767
    Verlag: Wiley
    Größe: 26725 kBytes
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Machinery Prognostics and Prognosis Oriented Maintenance Management

Chapter 1

1.1 Historical Perspective

With the rapid development of industrial technology, machine tools have become more and more complex in response to the need for higher production quality. While a significant increase in failure rate due to the complexity of machine tools is becoming a major factor which restricts the improvement of production quality and efficiency.

Before 1950, maintenance was basically unplanned, taking place only when breakdowns occurred. Between1950 and 1960, a time-based preventive maintenance (PM) (also called planned maintenance) technique was developed, which sets a periodic interval to perform PM regardless of the health status of a physical asset. In the later 1960s, reliability centered maintenance (RCM) was proposed and developed in the area of aviation. Traditional approaches of reliability estimation are based on the distribution of historical time-to-failure data of a population of identical facilities obtained from in-house tests. Many parametric failure models, such as Poisson, exponential, Weibull, and log-normal distributions have been used to model machine reliability. However, these approaches only provide overall estimates for the entire population of identical facilities, which is of less value to an end user of a facility [1]. In other words, reliability reflects only the statistical quality of a facility, which means it is likely that an individual facility does not necessarily obey the distribution that is determined by a population of tested facilities of the same type. Therefore, it is recommended that on-line monitoring data should also be used to reflect the quality and degradation severity of an individual facility more specifically.

In the past two decades, the maintenance pattern has been developing in the direction of condition-based maintenance (CBM), which recommends maintenance actions based on the information collected through on-line monitoring. CBM attempts to avoid unnecessary maintenance tasks by taking maintenance actions only when there is evidence of abnormal behavior of a physical asset. A CBM program, if properly established and effectively implemented, can significantly reduce maintenance cost by eliminating the number of unnecessary scheduled PM operations.

Prognostics-based maintenance, which is a typical pattern of predictive maintenance (PdM) has been developed rapidly in recent years. Despite the fact that fault diagnosis and prediction are related to the assessment of the status of equipment, and generally considered together, the goals of the decision-making are obviously different. The diagnosis results are commonly used for passive maintenance decision-making, but the prediction results are used for initiative maintenance decision-making. Its goal is minimum use risk and maximum life. By means of fault prediction, the opportune moment from initial defect to functional fault could be estimated. The failure rate of the whole system or some of the components can be modified, so prognostic technology has become a hot research issue. Now fault prediction techniques are classified into three categories according to the recent literature: failure prediction based on an analytical model, failure prediction based on data, and qualitative knowledge-based fault prediction. Artificial-intelligence-based algorithms are currently the most commonly found data-driven technique in prognostics research [1, 2].

Recently, a new generation of maintenance, e-maintenance, is emerging with globalization and fast growth of communication technologies, computer, and information technologies. e-Maintenance is a major pillar in modern industries that supports the success of the integration of e-manufacturing and e-business, by which manufactures and users can benefit from the increased equipment and process reliability with optimal asset performance and seamless integration

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