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Before going into technical details, it is important to identify key variables which determine the health of a battery. They are temperature, voltage and discharge. Once the variables are identified, batteries get equipped with sensors to gather the data about these parameters and relay it to the cloud for processing.

Sensor data cannot pass directly to the cloud – it goes through gateways. Field gateways are physical devices that filter and preprocess the data. A cloud gateway ensures safe data transmission and provides connectivity via various protocols, which allows connecting various field gateways.

iot in maintenance

Once sensor data enters the cloud part it “lands” on a streaming data processor. Its purpose is to allow continuous flow of data and quickly and efficiently transmit data streams to a data storage – a data lake.

A data lake stores the data gathered by sensors. It is still raw, so it may be inaccurate, erroneous or contain irrelevant items. It is presented as a number of sets of sensor readings measured at the corresponding time. When the data is needed for insights about battery’s health, it is loaded to a big data warehouse.

The big data warehouse stores cleansed structured data. It contains temperature, voltage and discharge parameters measured at a particular time and contextual information about batteries’ types, locations, recharge dates, etc.

Once the data is prepared, it is analyzed with machine learning (ML) algorithms. ML algorithms are applied to reveal hidden correlations in data sets and detect abnormal data patterns. The recognized data patterns are reflected in predictive models.

Predictive models are built, trained and then used to identify whether self-discharge occurs in a battery, pinpoint the batteries with capacity lower than normal or estimate batteries’ remaining useful life. The predictive models used for predictively maintaining industrial batteries are built based on two approaches"