Limiting Machine Failures with Holistic Views of Data

Summary

The manufacturing industry is facing a ton of pressure today. Companies need to do everything in their power to make life easier for their employees so processes and the production of goods are not slowed down. 
 
A significant culprit to delayed operations is machines themselves, specifically failing and broken machines. It is vital to ensure machinery stays functional to minimize as much downtime as possible. Every single year machine problems and defects cause interruptions to businesses, and when this happens time and time again, companies begin to see their bottom line negatively impacted.
 
A comprehensive and holistic view of all company data related to machines and their parts gives organizations the ability to identify patterns, ultimately keeping machines up and running to decrease downtime and save costs associated with halted production and repairments. 
 
To be proactive and identify optimal times for maintenance, organizations have been turning to artificial intelligence (AI). AI can consolidate all the data surrounding a specific machine or component and generate 360-degree holistic views for maintenance teams to act on.
 

What is predictive maintenance?

Predictive maintenance refers to the use of data-driven techniques that assist with analyzing equipment conditions and proactively identifying the best course of action to keep machinery functional and steady.
 
Rather than running manual machine checks on a daily, weekly, or monthly schedule or having repair teams come in once a problem has already occurred, the use of sensors can automatically monitor the conditions – temperature, pressure, humidity, and any other deviations from the optimal levels. The sensors can then alert management and the proper departments when a mechanical failure is likely to happen. Companies can prepare their teams well in advance, causing the machine to never skip a beat.
 

Using holistic views of data for smarter maintenance 

On top of the data collected by the machine sensors, a substantial amount of corporate information from many different data sources exists. This information comes from inventory lists, maintenance plans, logistics blueprints, and other manuals or technical documents. All this documentation may also exist in countless different sources – network drives, business applications, archives, document management systems, the cloud, the intranet, and more. Linking this data together with the data from the sensors can give you a full scope or holistic view of any given machine part, component, or process. In addition, company data is constantly being generated, requiring AI systems to continuously enrich the content viewed by maintenance teams when they search for a given entity.
 
Connecting data sources and continuously enriching the content of the results gives companies the benefits of being more productive, precise, and knowledgeable when working on a piece of equipment.
 
For example, if a repair team is alerted of a deviation in the optimal measurement of a machine, they will need to visit the factory and have a look. Let’s say the deviation is happening in an air compressor. AI allows the worker to search for an “air compressor” and every piece of relevant data across the company data sources will be extracted and presented to the user in an easily digestible format. Rather than trying to make a specialized fix from memory or searching through countless documents, anything the worker needs will be right there in front of their eyes in a matter of seconds. Now imagine the worker would like to get more specific and doesn’t require all information related to air compressors. For example, they can search “how to adjust part 457 on a certain type of air compressor?”
 
AI crawls the documents from structured and unstructured data sources and generates more precise answers to the problem in question. In-depth and specialized searches will produce interactive diagrams of the necessary components and give the user the exact knowledge they need for quick and efficient fixes, reducing downtime and optimizing labor.

Creating digital twins for holistic views 

A digital twin is an identical representation of a real-world entity or system. Enriched with data, digital twins allow maintenance teams to interact with different objects and components related to their task. Using the previous air compressor example, a digital twin of the broken part on the compressor can be created and enhanced with real-time data.

Digital twin technology generates a 3D-digital view and provides more knowledge to the worker than the physical part itself. Digitally, the worker can move the object around and receive insights about various sections or pieces related to the equipment. With AI and the worker analyzing the parts at the same time, the speed of knowledge finding increases significantly–just another way to avoid reworks, machine failures, and keep production moving at an efficient pace.