The Power of Machine Health

Augury -

May 13, 2024

Machines power the way we live. They produce many of the essentials we all need to survive and thrive – like food, medicine, and power.

The National Association of Manufacturers reports that unexpected machine failures cause a domino effect, resulting in about 10 percent of global productivity loss. When machines fail, this leads to a halt in production, increased waste, compromised worker safety, and more.

This proves the critical need for manufacturers to shift their focus to machine health – which helps predict and prevent failures while improving the performance and efficiency of machines, achieving sustainability goals, and increasing revenue. For the manufacturing industry, AI-powered solutions will be key in improving the health of the machines that power our lives.

The True Cost of Machine Failure

While machines can fail for many different reasons, their root causes can often be brought back to a combination of the following:

Poor data on machine conditions: Having a lack of prescriptive insights into the machine’s health leads to a lot of wasted hours digging through data to find the problem.
Lack of a single source of truth: Manufacturers typically maintain a messy ecosystem comprising maintenance, reliability, and operational systems and processes across domains – but they should adopt an integrated approach that ensures connected information for making better decisions.
Siloed teams: Some organizations have a culture where their teams, like maintenance, reliability, engineering, and operations, all function as different units – but they all should be working together, as one team aiming to meet the same goals.

Machine failures can get expensive, and quickly. Deloitte’s data: Industrial manufacturers lose $50 billion yearly due to unplanned downtime, an avoidable loss. While downtime can’t be avoided completely, it can be reduced significantly.

While the maintenance and reliability of machines are very closely related, they do have distinct differences. Maintenance focuses on performing maintenance during planned downtime and addressing unexpected shutdowns to fix failed equipment.

Reliability strategically analyzes root causes of failures, preventing their recurrence.

There are three different types of maintenance:

Reactive: This focuses on repairing an asset to its normal condition after a breakdown occurs or poor performance is spotted. This can be compared to waiting until the “low fuel” light comes on before gassing up your car, a habit many have experienced. While this approach can seem to be less costly, these “emergency” repairs can cost more than regular maintenance.
Preventative: Time-based maintenance, or scheduled maintenance, involves tasks performed at set times regardless of signs of problems. Those who change their car oil every 3,000 miles practice preventative maintenance, akin to industrial time-based maintenance. This is an effective way to periodically check machines and their conditions.
Predictive: When talking about predictive maintenance, requires the continuous monitoring of the performance and condition of machines. Newer cars collect data and alert you when you should complete a specific maintenance task. The goal here is to predict a problem before it happens, so manufacturers can prevent these failures from causing disruption.

Predictive maintenance has become a standard in the manufacturing industry but has also created a new problem – too much data, and no insights stemming from it. AI-powered machine health solutions can reduce this problem.

AI, with the help of machine learning, can interpret data from these machines that would usually require a human to do, learn it, and then apply that knowledge moving forward.

Advanced AI and IoT enable real-time machine monitoring, diagnosis, and issue resolution, preventing downtime and improving predictive maintenance. These machine health solutions can:

Diagnose problems, before they become too much of an issue.
Identify the type of malfunction – including why it’s happening, how severe it is, and the recommended solution.
Update diagnostics regularly.
Use data to continuously learn and improve functionality.
Enhance team productivity.

Embrace AI and Machine Health

Size doesn’t hinder organizations from adopting advanced industry technology. All manufacturers need to put machine health at the top of their priority lists, so they can meet the growing demands of the industry and stay ahead of the curve.

Manufacturers need to identify their goals first and then seek a partner equipped to help meet them to start this journey. Meeting goals around productivity, efficiency, and sustainability is more than possible – and technology, like AI and IoT, will be critical.