Abstract

This paper presents an innovative methodology, from which an efficient system prototype is derived, for the algorithmic prediction of malfunctions of a generic industrial machine tool. It integrates physical devices and machinery with Text Mining technologies and allows the identification of anomalous behaviors, even of minimal entity, rarely perceived by other strategies in a machine tool. The system works without waiting for the end of the shift or the planned stop of the machine. Operationally, the system analyzes the log messages emitted by multiple data sources associated with a machine tool (such as different types of sensors and log files produced by part programs running on CNC or PLC) and deduces whether they can be inferred from them future machine malfunctions. In a preliminary offline phase, the system associates an alert level with each message and stores it in a data structure. At runtime, three algorithms guide the system: pre-processing, matching and analysis: Preprocessing, performed only once, builds the data structure; Matching, in which the system issues the alert level associated with the message; Analysis, which identifies possible future criticalities. It can also analyze an entire historical series of stored messages The algorithms have a linear execution time and are independent of the size of the data structure, which does not need to be sorted and therefore can be updated without any computational effort.

Highlights

  • The concept of Predictive Maintenance [1–4] foresees the carrying out of maintenance activities before the equipment failure

  • The primary goal of predictive maintenance is to reduce the frequency of equipment failures by preventing the failure before it occurs [5]

  • The generic sensor is represented by its two constituent parts: the transducer (Ti) and the control electronics (CEi); this distinction is useful because even if the sensor is a unified whole, the transducer and the control electronics can be placed into different area of the machine tool

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Summary

Introduction

The concept of Predictive Maintenance [1–4] foresees the carrying out of maintenance activities before the equipment failure. The primary goal of predictive maintenance is to reduce the frequency of equipment failures by preventing the failure before it occurs [5]. This strategy helps to minimize breakdown costs and downtime (loss of production) and increase product quality, well known thanks to [6] and recently reiterated by [4]. Predictive maintenance is primarily about detecting hidden and potential failures It does not replace, but joins the Preventive Maintenance in the strict sense, which is linked to the execution of a specific protocol (often agreed with the machine manufacturer) intended to periodically.

Industrial machine tool data
The model for machine tool
System functionality
Pre-processing design phase
Runtime phase
Detailed algorithms description
Performance analysis of algorithms
A simple case study
Case study data description
System prototype run and results
Conclusions
Full Text
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