Things you should know about IIoT & Predictive maintenance
How does it work in real-time?
Real-time sensors installed on equipment provide real-time data that you can feed into predictive models to help determine when something is about to fail or what the remaining useful life is for that equipment. You can then schedule maintenance based on that data.
With the help of Industrial IoT for predictive maintenance, we get insights into the condition of the equipment – under a variety of scenarios – and plan interventions. We can take data from a wide range of data sources; and may even combine weather information with equipment data, and then determine when to send a crew for Predictive Maintenance.
Where does Machine Learning come in?
Using IoT, we can often end up collecting massive amounts of data. For the human eye to make sense of it becomes a real challenge. Machine Learning is used, in conjunction with IoT, when traditional data analysis and mathematical models are not enough to translate data into actionable insights. You can call it automation of analytics.
Benefits
In his blog on “How IoT-enabled predictive maintenance can transform your business,” by Tom O’Reilly, GM IoT Device Experience, Microsoft, writes –
Predictive maintenance is an application that aggregates environmental, process, and resource data and uses AI and machine learning to analyze and predict when an asset needs to be maintained or replaced before a failure occurs. Benefits may include:
- Reduced unscheduled downtime: Avoid costly equipment failures and unscheduled downtime. Proactively address issues before they become problems that significantly impact operations.
- Increased quality: Improve products and processes through machine learning and detect maintenance issues early to increase customer satisfaction.
- Decreased costs: Lower maintenance costs and extend equipment life.
- Greater efficiency and output: Increase process efficiency, asset utilization, and production output.
Where do you start?
We must first understand that what we wish to predict should be something we can take action on—for the prediction to have business value. Microsoft, in the description of their widely used Azure IoT platform, gives a simple set of questions we must ask ourselves.
- Timing: How much time does the equipment have left until it fails?
- Probability: What is the probability of failure in (x) number of days or weeks?
- Cause: What is the likely cause of a given failure?
- Risk-level ranking: What equipment has the highest risk of failure?
- Maintenance recommendation: Given certain error code and other conditions, what maintenance activity is most likely to solve the problem?
Looking for help in choosing the right platform for Predictive Maintenance, and build the right solution? Talk to Hakuna Matata.
