How to leverage predictive maintenance for operational efficiency
13 September 2017
Today, predictive maintenance is widely considered to be the obvious next step for any business with high-capital assets looking to harness machine learning to control rising equipment maintenance costs.
Connectivity spoke to Jennifer Roubaud, UK and Ireland Country Manager for Dataiku, who guides us through some of the challenges organisations might face and advice's how best to get started on the predictive maintenance journey.
Firstly, what is predictive maintenance?
Predictive maintenance takes data from multiple and varied sources, combines it, and uses machine learning techniques to anticipate equipment failure before it happens.
Many businesses are already using continuous monitoring technologies - like Internet of Things (IoT) connected devices - which is a good start; but the key lies in not just simply monitoring the output of various data (which is how many companies use it today), but by taking the next step and employing advanced algorithms and machine learning to take action from real-time insights.
With multiple sensors dotted around sites across the world, one of the biggest challenges for organisations today is gathering an amalgamation of data from numerous assets and then using that data to detect and derive useful, actionable insights; they often don't know where to start.
The seven steps of predictive maintenance
The following steps outline the process organisations need to embrace to make predictive maintenance effective within the short term and also to prepare for the long-term changes and benefits it can bring:
Step 1: Understand the need
The first step in moving toward predictive maintenance is to understand pain points (namely drivers of costs, waste, or inefficiency) and identify the best use case for your business.
Step 2: Get data
Of course, the proliferation of IoT plays a large role in predictive maintenance, especially with cheap sensors and data storage combined with more powerful data processing that has made the technology accessible. But, there are other data sources out there, which might include:
• Data from programmable controllers
• Manufacturing execution systems
• Building management systems
• Manual data from human inspection
• Static data, like manufacturer service recommendations for each asset
• External data from APIs, like weather, that could impact equipment conditions or wear
• Geographical data
• Equipment usage history data
• Parts composition
Step 3: Explore and clean data
After identifying relevant data sets, it’s time to dig in. Ensure you really understand all the data you’re dealing with and that you know what all of the variables mean, what is being measured, and where all the data is coming from.
Step 4: Enrich Data
Manipulating data at this stage means adding more features and joining it in meaningful ways so that each data set, or data from multiple sensors, can be taken as a whole instead of in parts.
Step 5: Get predictive
It is precisely this combination of a variety of sources and data types that allows for the most robust and accurate predictive models. The more sources and types of data available, generally the better the complete picture of a particular asset and the better the prediction.
Step 6: Visualisation
Visualisation is an important tool in predictive maintenance as it often closes the feedback loop, allowing maintenance managers and staff to see the outputs of predictive models and direct their attention accordingly. Robust data science or data team tools today allow maintenance managers and staff on-the-ground to easily access and digest outputs in a familiar format so that the entire team - from analysts to technicians - receive the same feedback.
Step 7: Iterate and deploy
Deploying a predictive maintenance model into production means working with real time data, but to iterate and deploy means providing visual real time dashboards for on-the-ground maintenance teams. For some use cases, feedback can be integrated directly into the predictive maintenance process, requiring no (or little) human interaction.
Secondary analytics
Because taking high-capital assets out of service can be extremely costly in and of itself (even when compared to the benefits of identifying necessary maintenance before run to failure), the next questions are when and how? This is where secondary analytics come in.
Ultimately, the goal of secondary analytics following predictive maintenance is to determine a plan of action for exactly when the asset should be taken out of service so as to minimise disruption and loss (both imminent and future) and maximise resources.
Next steps
The biggest initial win with predictive maintenance initiatives is cost savings. But after implementing a larger, more robust, and more mature predictive maintenance strategy, larger opportunities begin to open from a business perspective, and high-value assets can bring in some additional revenue instead of just being costs.
Predictive maintenance also lends itself to the future of artificial intelligence (AI), where operations will be entirely self-maintenance with very little human interaction whatsoever. AI in the predictive maintenance space would go one step beyond the steps discussed above, which would still require some manual analysis of models and outputs. These systems will watch thousands of variables and apply deep learning to find information that could otherwise be undetected that might lead to failure. Ultimately, predictive maintenance isn’t so far off from AI, and businesses that get started with predictive maintenance programmes now will be well-poised as market leaders in the future.
Collaboration is key
Jennifer emphasises that no predictive maintenance strategy can be truly effective without collaboration between the IT and business sector of an organisation. Most have realised the largest cost savings from predictive maintenance can be achieved by rethinking and optimising the entire maintenance strategy as a whole from top to bottom. This means creating a collaborative platform where people with different skill sets can work together. A joint effort between business operators and data scientists/analysts means unnecessary maintenance can be prevented, replaced instead with planned maintenance that saves costs and reduces downtime.
If you wish to learn more about implementing predictive maintenance, data science company Dataiku, has published a free guidebook. Download for free: “How To: Future-Proof Your Operations with Predictive Maintenance”.
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