Integration of EAM systems with equipment in the factory

One of the main tasks of the EAM system is the maintenance and repair (MRO) of equipment. It is solved by different methods, depending on the criticality of the failure of a particular production asset for the enterprise.

Inexpensive and easily replaceable equipment allows to work to failure (reactive type of service).

For equipment, the downtime of which for production is low, and it is repaired quickly, a preventive type of service or scheduled preventive maintenance (PPR) is assigned. These repairs are carried out according to previously developed plans, regardless of the technical condition of the equipment.

In the general case, the methods of technical diagnosis are divided into subjective and objective.

Subjective simply means inspecting equipment. Inspection includes visual inspection, assessment of noise and vibration, determining the degree of heating of equipment components, detecting leaks in seals, etc. 1 And although instrument methods provide much higher accuracy and efficiency, at the first stage of solving diagnostic problems it can be very useful the experience of a mechanic who knows the features of the operation of a particular piece of equipment.

Question: But what about the mechanic’s experience integrating with the EAM system? Mobile applications serve this purpose. 2 Having scanned a QR tag on the equipment using a smartphone or tablet, the repair worker can not only enter the database, even if not the most accurate, but up-to-date, but also immediately get the information he needs about suspicious piece of equipment.

Question: But what about the mechanic’s experience integrating with the EAM system? Mobile applications serve this purpose. 2 Having scanned a QR tag on the equipment using a smartphone or tablet, the repair worker can not only enter the database, even if not the most accurate, but up-to-date, but also immediately get the information he needs about suspicious piece of equipment.

But, of course, to obtain a quantitative assessment of the measured parameter is possible only with the help of instruments. The technical tools used to diagnose equipment can be divided into portable, analyzers and embedded systems.

If the cost of downtime of a piece of equipment is estimated to be high, a predictive (forecast) type of service is assigned to it. The condition of such a production asset is diagnosed and monitored daily or even continuously.

The efficiency of using the EAM system at the enterprise largely depends on the speed and accuracy of equipment diagnostics, i.e. from the degree of integration of the EAM system with the equipment, and this applies to all types of services. Even a light bulb in a dark place must be replaced as soon as possible in order to avoid injuries.

Portable technical diagnostic tools are designed to measure one or more parameters. Examples include devices such as an electronic stethoscope, vibrometer, tachometer, pyrometer. Operational transmission of information, as during inspections, can be carried out through mobile communications.

Analyzers allow not only to measure, but also to analyze diagnostic parameters. The information obtained makes it possible to detect failures at an early stage of development. Examples include devices such as vibration spectrum analyzers, thermal imagers, etc. The analyzer provides the collection and preliminary analysis of data, based on the results obtained, it is possible to carry out deeper studies in the future using special software.

Built-in diagnostic systems are used when continuous monitoring of the technical condition of equipment is necessary. With their help, it is possible to solve problems such as protecting equipment from abnormal operating conditions, monitoring an extensive set of diagnostic parameters, and promptly diagnosing the state of production assets. That is, it is the built-in diagnostic systems that provide the most tight integration of the EAM system and equipment.

Until recently, the main drawback of the integrated diagnostic systems was their high cost. It included not only the costs of acquiring and installing sensors, ensuring the transmission of information from them, but also the costs of servicing a complex multicomponent system. This limited the use of built-in diagnostic systems; they were used by no more than 10% of the equipment in use.

In addition, in the “upper level” systems, such as ERP and EAM, the information must come in the form corresponding to the logic of these systems. Therefore, an intermediate solution was often required for structuring the collected data, their aggregation, processing and analytics. 4 This entailed additional costs for hardware, software and highly qualified personnel.

But as the Internet of Things (IoT) and especially the Industrial Internet of Things (IIoT) evolve, things change. The price of sensors drops rapidly, their capabilities grow, modern wireless data transmission systems are cheaper than wired and deploy faster. Thanks to the advent of low-cost processors, it becomes possible to collect and process more and more equipment data even before they are transferred to ERP-class systems (i.e., carry out “foggy” or “boundary” calculations with built-in analytics), which simplifies pairing the information collection system with ” top-level “systems, which means it simplifies the integration of equipment with EAM.

As the volume and quality of information on the technical condition of equipment grows, it becomes possible to use advanced analytical tools for processing this information: artificial intelligence and machine learning. And this means taking EAM systems to a qualitatively different level when they will reveal hidden patterns inaccessible to traditional methods of analytics. The knowledge obtained as a result can increase the value of the production assets of the enterprise by achieving the maximum possible overhaul period of equipment operation and increasing the efficiency of its use.