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IBM Predictive Maintenance and Quality 2.0 Technical Overview

An IBM Redpaper publication


Published on 29 June 2015

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ISBN-10: 0738454257
ISBN-13: 9780738454252
IBM Form #: REDP-5035-01

Authors: Vrunda Negandhi, Lakshminarayanan Sreenivasan, Randy Giffen, Mohit Sewak and Amaresh Rajasekharan

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    This IBM® Redpaper™ publication updated technical overview provides essential details about the data processing steps, message flows, and analytical models that power IBM Predictive Maintenance and Quality (PMQ) Version 2.0.

    The new version of PMQ builds on the first one, released in 2013, to help companies efficiently monitor and maintain production assets and improve their overall availability, utilization, and performance. It analyzes various types of data to detect failure patterns and poor quality parts earlier than traditional quality control methods, with the goal of reducing unscheduled asset downtime and improving quality metrics.

    Version 2.0 includes an improved method of interacting with the solution's analytic data store using an API from the new Analytics Solution Foundation, a reusable, configurable, and extensible component that supports a number of the solution's analytic functions. The new version also changes the calculation of profiles and KPIs, which is now done using orchestrations that are defined in XML. This updated technical overview provides details about these new orchestration definitions.

    Table of Contents

    Chapter 1. Introduction

    Chapter 2. IBM Predictive Maintenance and Quality

    Chapter 3. Solution architecture

    Chapter 4. Master data loading

    Chapter 5. Event mapping and processing

    Chapter 6. Sensor analytics

    Chapter 7. Maintenance analytics

    Chapter 8. Integration analytics

    Chapter 9. TopN Failure analytics

    Chapter 10. Statistical process control support

    Chapter 11. Inspection and warranty analytics

    Chapter 12. Integrating IBM Maximo Asset Management

    Chapter 13. Additional reports

    Appendix A. Execution, training, and deployment of SPSS models


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