IoT enabled Predictive Maintenance
Updated: Jan 9, 2019
Predictive maintenance software solutions will execute the real time analysis of multiple data sources to predict asset failure or quality issues. Eventually your company can avoid costly downtime and reduce maintenance costs. Since these solutions are powered by predictive analytics, it can detect even minor anomalies and event patterns that can cause the asset failure. It can also determine the assets and operational processes that are on the verge of problems or failure. Pro-active identification of potential failure concerns helps you to execute the following:
Cost effective resource allocation
Increased equipment uptime
Improve quality and supply chain process
Elevated customer satisfaction & engagement
Learn from machine behaviour to enhance the product's feature list
Our end to end IoT enabled predictive maintenance solutions for you!
We would like to propose a solution that can predict asset/equipment failures proactively and execute the corrective measures before they occur. By bringing your customer network under one hub, with the help of an IoT framework or in other words by integrating cloud solutions to maintain a network of customer devices. We can perform 4 steps: Collect, Analyse, Predict & React.
Collect: With the help of integrated cloud solutions operational data can be collected from your customer network.
Analyse: Initially, we must start with a question. Can we detect an equipment failure that is likely to happen in the near future? Determine the parameters that dictate if the equipment is failing or not. We must have a good large data set, that can provide insights of when the equipment may fail and when they operates normally. We would like to propose a strategy to accomplish this: Collect data from all the machines running in normal conditions. When a failure occurs, we can capture the data during a specific period before the machine failed. This new set of data collected will be used to predict the failures.
Predict: We need to build a model to answer this question. We have the data sets, data collection processes and complete analysis in hand. With our expertise in neural networks & Machine learning techniques, we can identify the equipment parameters determines failures in our equipment. Training of this model can be done in cloud. Prediction confidence- This output can be used to determine when the corrective maintenance can perform on the equipment. This can be scale up to wide set of customer base.
React: Along with the prediction system in place, a reaction engine is also there to make decisions and generate notifications. When the data collected, passes through the trained model, it can decide the equipment's operational quality: whether it is working normally or likely to experience a failure with in a short span of time. If in case of a failure, notify the stakeholders immediately.
Sharing couple of our engagements!
1). Predictive Analytics to predict the maintenance schedule of machineries
We have provided consultation service to the state electricity board. Objective is to deploy a predictive analytics strategy to schedule the maintenance of machineries.
Business Issue: Need a system to manage the maintenance schedule efficiently. Client want to know more about the predictive maintenance and the possible options to implement that.
Our Solution: We designed a predictive analytics strategy and implemented a POC to analyse the ROI and feasibility. Overview of our strategy:
Analysed the maintenance history of various machineries
Identified a pattern
Predict the maintenance schedule by considering the machinery type and geographical attributes
We introduced a connected network of machineries and the whole maintenance details can be pulled out from any location
Value Addition: The maintenance cost has been decreased because of the selective approach. Downtime also reduced. No need to do the periodic maintenance for the machineries in a single shot.
2). Advanced Data Analytics solutions for IoT network of pollution detection sensors
Business Issue: IoT based product company focused on pollution monitoring. They need to analyse the environmental factors which pave the way for high pollution levels. They need insights to provide the analysis reports back to the governing bodies and concerned authorities.
Our Solution: We created a data network from the moving sensors deployed on vehicles and various locations within the city to constantly monitor pollution and environmental factors. Our solution is powered by the AWS platform.
We will store the collected data from various sources in S3 bucket. Then extract raw data stored in S3 bucket using python and boto3. Extracted data from s3 bucket is cleaned and stored as data frame. We will push it to amazon redshift. Finally an API will also delivered back to client in order to access this.
Predicted the pollution level of a particular region based on the weather information of a different location with matching parameters of PM value, Temperature and Pressure
Benefits for client: From this structured and cleansed data, we can derive meaningful insights to develop analysis reports. Predicted the weather information of different location by analysing the trend of a particular location. We developed Power BI based dashboards to visualise the data and insights.
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