Machine learning based analysis of historical sensor data can detect the need for preventive maintenance before the same may be evident to human operators, thereby allowing one to proactively schedule maintenance to prevent costly outages. The resultant avoidance of last-minute repairs and part replacements go a long way towards achieving maximum operational efficiencies in the production line.

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Sensor data analysis is a key process for the detection of anomalous readings and hence is a key indicator of abnormal operating conditions of the equipment under review. The source of the abnormalities could be environments or inherent in the equipment itself – either ways, such anomalous operating conditions are often the precursors of equipment failures and hence, early detection and suitable escalation to human operators for investigation is critical to smooth operations.

Sensor Data


With the proliferation if IOT-based sensors, it may seem that we have achieved the nirvana stage and that all data will automatically become available for analysis and display; however, a lot of pre-processing and data cleansing activities need to be carried out, at scale, in order to unlock this potential value. The presence of a wide variety of sensors and data collection mechanisms make this doubly complicated; an experienced partner like us can help deploy the data collection, cleansing and storage mechanisms that form the base for further machine learning and analytics.

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Feature Extractor for Time Series

Developed a feature extractor for the time series data on Current, Temperature and Vibration readings from various sensors using Python packages (NumPy, Pandas).

Rules Engine

Deployed a rules engine based on time series analysis to automatically detect problematic events of interest and to generate alerts based on the detected events. The alerts were automatically sent as mobile app notifications and as emails.


The resultant model was deployed to the customer’s on-premises infrastructure and the generated alerts and dashboard graphs were pushed to a React-frontend website, deployed on the cloud.

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