Welcome to the holy grail of maintenance solutions – Predictive Maintenance (or PdM).

If you’re reading this post, it’s likely you’re already familiar with the concept of Predictive Maintenance solutions. You’ve begun your due diligence. You understand it’s one of the most effective ways to optimize the maintenance and performance of your assets (not to mention your bottom line).

Management consulting firm McKinsey & Company notes its industry-transforming power:

“We estimate that predictive maintenance could reduce maintenance costs of factory equipment by 10 to 40 percent. Such savings would also be possible in health-care settings. Additionally, better predictive maintenance using IoT can reduce equipment downtime by up to 50 percent and reduce equipment capital investment by 3 to 5 percent by extending the useful life of machinery. In manufacturing, these savings have a potential economic impact of nearly $630 billion per year in 2025.”

In his book, The Plant Engineer’s Handbook, Keith Mobley further documents that predictive maintenance can:

predictive maintenance capabilities

Even if you assume these to be “best case” results, generating a fraction of them can mean millions of dollars in increased performance for any manufacturer.

And if that weren’t enough, predictive maintenance offers an important associated benefit. It increases overall workplace safety; reducing risks associated with malfunctioning equipment and boosting worker efficiency.

These are no small impacts for any manufacturing business.

They are benefits that simply can’t be had from reactive or preventative maintenance programs.

Adoption of PdM Has Been Shockingly Slow

Despite these documented financial, performance and safety benefits, predictive maintenance has been slow to catch on. A recent study by Bain & Company revealed this unexpected trend:

“Two years ago, predictive maintenance was forecast to be one of the most promising uses of the industrial Internet of Things (IoT). … So it’s somewhat surprising that predictive maintenance has failed to take off as broadly as expected. A recent Bain survey of more than 600 high-tech executives has found that industrial customers were less enthused about the potential of predictive maintenance in 2018 than they were two years earlier.”

Why would any business be slow to adopt a technology with such incredible ROI potential?

At Agosto, we believe this reluctance among manufacturers is the result of a combination of factors.

In this article we will briefly explain those factors. Then offer solutions manufacturers can consider to overcome them when implementing a proof of concept installation.

The Biggest Barrier to PdM Implementation

At its most basic level, the requirements for an effective predictive maintenance program are fairly straightforward. You need sensors, data, and a processing platform.

But that’s where the simplicity ends. Predictive maintenance gets its “super powers” from a level of sophistication unlike any other maintenance strategy.

It leverages the power of big data along with artificial intelligence (AI) and machine learning (ML).

That means, by default, there are no “cookie cutter” solutions. No one-size-fits-all monitoring system you can unpack, plug in and start generating same-day results in your factory.

And that is compounded by another reality of manufacturing businesses.

Top manufacturers, who can benefit the most from predictive maintenance, often have the most customized manufacturing processes.

It’s what sets them apart in their market. And gives them a competitive edge.

That means that not only are there a large number of factors to consider regarding the solution itself, but all those factors need to be customized to every manufacturing environment.

These “customization barriers” can often seem overwhelming to any manufacturer thinking about a predictive maintenance solution.

However, by understanding four fundamental considerations, you can simplify the process greatly.

We’ll examine those four considerations in the rest of this article.

First Things First: Creating a PdM Strategy

The first thing any business considering a predictive maintenance strategy must understand is that implementing it is a process – NOT a project.

The goal of any implementation should be to start small while developing a “repeatable playbook.” One that can be cost-efficiently scaled for the other assets in your company.

Here at Agosto we recommend our clients employ a “lean” implementation strategy. One that will let you implement at the lowest possible barrier. Validate your proof of concept. And then scale up from there.

Here are four areas where you’ll need to consider your customization needs.
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Consider the Assets You Want to Monitor

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Determine the Failures You Want to Predict

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Assess the Data You’ll Need to Collect

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Define the Models You Need to Run

1. Consider the Assets You Want to Monitor

The good news where predictive maintenance goes, is that you don’t have to implement it across your entire business.

In fact, you shouldn’t.

In many cases, more expendable production assets may actually be better served with a preventative or run-to-failure maintenance strategy.

For PdM, you want to focus on your most critical assets. In general, determining these is pretty straightforward.

Equipment where an unexpected breakdown would slow or stop production entirely is an obvious consideration. Especially equipment that needs to run on a near constant basis with little to no downtime afforded for maintenance.

But there are other factors you should consider when selecting customized equipment for your proof of concept.
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Assets with higher failure rates or maintenance needs.
These can be ideal candidates as well. Predictive maintenance can not only reduce downtime, it can also help uncover the root causes of frequent failures.

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Equipment with expensive or hard-to-source replacement parts.
The goal of any business is to minimize inventories of replacement parts. Fixing or maintaining these machines becomes especially costly when these parts have to be ordered on short notice.

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Consider remote assets.
For equipment in hard-to-reach or even dangerous locations, predictive maintenance can allow for more frequent and safer monitoring.

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Difficult to replace assets.
Would replacing a roof top fan require bringing in heavy equipment like a crane? Any asset whose replacement carries added costs or planning time can be a good candidate for your predictive maintenance trial.

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Finally, don’t limit your exploration to your most expensive equipment.
Critical assets come in all shapes and sizes. A $200 fan motor that is essential to the operation of an entire production line may be just as important as the million dollar machines on the line.

2. Determine the Failures You Want to Predict

The goal of predictive maintenance is generally understood to be preventing catastrophic failures that grind businesses to a halt. A better way to understand it, however, is as a means to manage failure.

Every piece of machinery you have is eventually going to fail.

One of the biggest challenges to manufacturers’ maintenance programs is determining when to replace components or entire assets. This challenge increases with the complexity and customization of a manufacturer’s production system.

When components of an asset operate in unison to produce an outcome, it becomes exponentially more difficult to determine which and whether a component within the system should be repaired or replaced in a timely manner (not prematurely) so the whole system stays up and running.

The goal of predictive maintenance is to quantify the risk of a specific failure at any moment. And then use this data to manage the effects of that failure

If you can effectively plan for equipment failures in advance, you can achieve all the benefits predictive maintenance offers.

Understanding that, there are 3 basic failure outcomes predictive maintenance can predict for:
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Estimating the remaining useful life of an asset:
This failure projection involves making predictions about the time remaining before a component or piece of equipment will need to be replaced.

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Predicting whether an asset will fail within a specific time frame:
Again, predicting this doesn’t reduce the incidence of failure. It does,however, give you time to organize resources, order replacement parts, and minimize downtime.

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Detect and predict anomalies within a complex system that may lead to failure:
Anomaly detection is unique in that its purpose is to discover mechanical behaviors that are not part of the equipment’s historical data. In essence, it’s trying to predict the unknown.

This type analysis can take one of three courses:

It can recognize established anomalous patterns within your assets and signal when they occur.

It can attempt to establish “unrecognized” spikes and dips – deviations from the normal range of operating data – and report them as anomalies.

Finally it can track minimal changes over time until they become anomalies. This analysis presents a challenge of its own as it is not “pattern based” like the previous 2 models. Here performance degradation takes place over time making the resulting anomalies more difficult to spot.

You should start by selecting only one failure outcome to manage. Ideally those that best match the needs of your current production.

3. Assess the Data You’ll Need to Collect

Effective predictive maintenance is a function of machine learning. The key to effective machine learning is access to data. Lots of data.

But don’t let this requirement put you off.

Like we’ve said before, no one’s data is perfect, so start with the data you have.

“If you’re like most manufacturers, you already have a rich store of data for machine learning to use, including MES, PMM or CMMS data, your ticketing system and other databases across the company.”

In general, specific data requirements will depend on the type of failure you want to monitor for. All of it, however, should be obtainable from one of 4 sources:

Historical failure data: Failure data is one of the data types critical for predictive maintenance. Yet it can be one of the most difficult to come by.

A lack of this type of data is not uncommon. It’s often due to preventative maintenance systems which over-perform maintenance (while increasing costs).

So what to do?

To overcome this obstacle, it’s possible to simulate failure data to train your algorithms.

Consultants with extensive systems knowledge can leverage strategies such as Failure Mode Effects Analysis (FMEA). Following these steps, they can adjust factors like temperature, vibration, and flow rates under certain scenarios to simulate potential failure scenarios. This data can validly be used in place of actual failure data.

Historical maintenance and repair data: These data include records of components that have been repaired or replaced. Recent inspections should be noted as well.

Machine operating data: This is data collected under normal operating conditions. There should however be enough to contain variances that capture the aging process of the equipment being measured as well as any potential anomalous data that could also lead to degradation. In other words, it’s data collected under both normal and faulty operating conditions.

Equipment meta-data: This refers to “static” data about the actual equipment being monitored. It includes information such as model or version numbers, manufacture date, operational settings and the like. This data never changes relative to the performance of your equipment. But it can add a powerful predictive advantage to your data.

One final note: Data calculations at scale are typically done by leveraging a cloud implementation. This does not need to be the case when testing. Data computations can frequently be done “at the edge” – on a small amount of local infrastructure deployed for inference purposes. This can be a preferred solution to start out. Especially when data transfer costs are high.

4. Assess the Data You’ll Need to Collect

When considering the type of models that will best suit your needs, it’s first important to understand the basic types of calculations predictive maintenance uses.

Predictive maintenance math essentially falls into one of two categories:

      • Condition monitoring
      • Predictive analysis

Condition monitoring models use data obtained directly from machinery. These include factors like temperature, vibration, fluid pressure and so on. Its goal is to assess a machine’s condition at any point in time and diagnose possible faults.

To do this, it tracks condition indicators which are data of the system that can predictably be measured as the system degrades. It groups similar clusters of performance data together. It then diagnoses potential faults by comparing incoming data against fault benchmarks.

Predictive analysis forecasts when a failure will occur based on past performance data. This model looks to predict future values of condition indicators based on the current and past states of the machinery.

Predictive maintenance uses a combination of these calculations to analyze and report on the data collected from the system.

Depending on the failures you want to predict for, you’ll need to consider implementing one of two main models.

Regression models: These models are best used for estimating remaining useful life (RUL). They look at dependent variables in conjunction with independent variables to measure the correlation or “strength” of the relationship between them.

They return a specific value for the likelihood of an outcome occurring.

Classification models: These models come in two flavors: “Binary” and “Multi-class.” They are typically used to predict whether an asset is likely to fail within a certain time period. Rather than returning the likelihood of a single outcome, these models seek to answer a “yes or no” question – will a piece of equipment fail with X time frame – with a particular degree of certainty.

The model you choose will be largely determined by the results that best serve your manufacturing process.

The Bottom Line: Leverage the Expertise You Need

As you can see, there’s a lot to consider when thinking about a predictive maintenance program.

And much of it depends on the complexity and degree of customization of your manufacturing processes.

But these considerations don’t have to stand between your company and an effective predictive maintenance program.

This article is not meant to be a comprehensive guide in solving these dilemmas. In truth the vast number of possibilities are beyond the scope of any online article.

What it hopes to do, is to shed some light on the customization barriers that must be considered when implementing a predictive maintenance proof of concept solution. Also, to step you through the major points you need to be aware of when designing a predictive maintenance solution for your business.

There are any number of hurdles a business can face when seeking to leverage a predictive maintenance solution.

But the only real barrier is when implementations are done without the requisite expertise to properly integrate them into a highly customized manufacturing processes.

Leveraging the expertise of your in house team – as well as external resources – to determine the specific needs for your business will make implementing your proof of concept, and ultimately a full blown predictive maintenance process into your custom business environment a snap.

If you have more questions, we’re happy to answer them.