The Next Generation of Condition Monitoring Capabilities
Advancements in Predictive Maintenance and IoT Integration
When it comes to connecting, collecting and analyzing data, we’ve seen macrotrends among many industries that include the democratization of machine learning, increased accessibility of edge compute resources and the proliferation of connected devices or sensors.
Organizations no longer need an internal data science team. They also now have access to all kinds of information about their assets. However, many still don’t know how to make the most use of this data, including only utilizing reactive maintenance operations instead of predictive.
According to a U.S. Department of Energy Operations and Maintenance report, instituting a proper preventive maintenance program can result in energy savings of as much as 12%.
In addition to energy savings, we’ve seen organizations that implement a predictive maintenance solution improve:
- Long-term value and scalability with a common manufacturing data fabric
- Asset availability by 10-30%
- Plant productivity by 10-15%
- Total cost of ownership from 8-12% up to 40%
- Worker safety, all but eliminating any catastrophic failure
Asset Performance Management and Predictive Maintenance
Asset Performance Management (APM) encompasses the capabilities of data capture, integration, visualization and analytics tied together for the explicit purpose of improving the reliability and availability of physical assets. Predictive Maintenance (PdM) is a component of an APM system that utilizes AI and Machine Learning (ML) techniques to try and predict asset health issues before they arise.
This includes the components of:
- Condition-Based Monitoring
- Operations Management
- Reliability-Centered Maintenance (RCM)
PdM uses condition-based data from various sources to deliver real-time insights on asset performance. This type of solution is designed to improve the reliability of assets and maintenance strategies using a combination of first-principles analysis and data science.
Our Approach to Maintenance
To have effective maintenance operations, organizations need to understand the fundamentals.
At the most basic level, these include planning, scheduling and execution. How well an organization can do these three activities establishes the baseline for how impactful every other maintenance program, technology choice or initiative can be.
Now let’s dive a little deeper into each of the concepts of predictive maintenance we listed above.
Condition Monitoring
How do I get my assets to talk to me and tell me when they’re “unhealthy”?
This includes device-level analytics that use advanced sensing, edge computing and machine learning. It can tell me something about the mechanical health of an asset driven by variable frequency drives.
Consider predictive maintenance driven by condition monitoring. Although a trendy term, its roots are decades old. Condition monitoring solutions, like Dynamix, work with monitors and portable data collectors to help establish and execute a condition-based predictive maintenance program. It helps a maintenance team detect oncoming failure earlier and more accurately.
Eventually, a maintenance request is generated by the condition monitoring software or team, and it enters nearly the same planning, scheduling, and execution workstream as every other maintenance request. If the maintenance team is excellent at planning, scheduling and execution of other maintenance requests, they will likely be excellent at doing the same for a request created by condition monitoring.
A solution like Guardian AI is a differentiator in the market because it incorporates signals from existing sensors to apply pre-built ML models to the data source and close gaps.
Condition monitoring can give that team a longer time horizon to get all those pieces in place before failure occurs. This brings us to operations management.
Operations Management
How do I manage the action-side of things and make sure I get the right insights at the right time?
Data born from devices, orchestrated at the edge, and liberated in the cloud, make data available, meaningful, useful and valuable.
This component includes data contextual like visualization for real-time asset tracking. Think about supervisory analytics with APM, CMMS, IIoT.
It’s never been enough to just detect failure. Being able to get technicians and contractors on site, with the right training and information, with the right tools and materials, the right safety permits and at the optimal production window are the fundamentals in action.
This is where technology like DataMosaix comes in. It is custom designed for industrial data applications and specializes in providing contexts around the multitude of the condition monitoring, the solutions existing in the sensing and control level, and the ability to add context and take actions against supervisory or operations management level.
Reliability-Centered Maintenance (RCM) Consulting
Where should I focus my efforts?
RCM consulting includes the strategy, delivery and support services that ensure you’re focusing on the right opportunities. We do more than provide solutions. We help orient the solution around the really valuable things for an organization to go after.
This includes focusing on questions like where are the critical assets that are bottlenecks or what issues are safety critical?
High-Value Opportunities for Predictive Maintenance
Results in terms of throughput or maintenance costs are highly dependent on the business processes you have in place, the specific assets you're running and the product you're making. We’ve worked with organizations across many different industries, including consumer packaged goods, tire, oil & gas, aerospace and mining.
For example, process and heavy industries working with remote assets are probably the most mature in terms of maintenance operations, but not in terms of connected operations. There’s still room to deploy ML algorithms and make use of the sensor equipment available. In pharmaceutical manufacturing, the ambient environment is important. A HVAC or cooling tower going down can lead to scrapping, reworking or loss of valuable product.
Learn more about each of the opportunities we identified in each of these industries below:
Why Now?
Everyone has a maintenance department with varying levels of maturity. When it comes to utilizing this kind of solution, companies often struggle with competency, talent retention and training challenges.
With major advancements in edge computing and analytics, there’s an opportunity to embed a layer of innovation into your intelligent devices that work with machine learning.
It’s hardware, it’s software and it's services to make it real, all under one roof. It’s the next generation of condition monitoring capabilities.
If you haven’t already, check out our article on selecting use cases that will add value quickly while generating enough momentum to sustain an advanced analytics program.
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