Vibration Classification with BrainChip's Akida

With predictive maintenance, you can monitor your equipment while it’s running: This means that there is less downtime for inspections and repair jobs because the monitoring process takes place during operation instead of waiting until something breaks or wears out.

The Edge Impulse platform and solutions engineering team enables companies to make more accurate predictions about when devices might fail, which lets them optimize their fleet maintenance and use service crews most effectively. This saves the companies money by letting them lower overall asset downtime and allows customers to be more satisfied with their product and services.

In this article, we will explain some of the beneficial applications of predictive maintenance, and then show how to build a predictive maintenance solution that will detect abnormal vibrations using Edge Impulse’s platform, the BrainChip Akida hardware, and a computer cooling fan.

Business Case Examples for Edge Predictive Maintenance

Predictive maintenance provides a wide variety of business benefits, such as:

Predicting asset depreciation and maintenance timelines The security and building-access industry have been experiencing increasing pressure due to the global pandemic, and it’s imperative for customers to understand when a security door or component might fail. By anticipating maintenance, companies can reduce unplanned out-of-service intervals, allowing for minimal disruption in office buildings where there is huge traffic of people.

Lowering cost and gaining more ROI Global shipping companies are looking for ways to lower their costs and increase efficiency. Focusing on predictive maintenance can allow them to proactively address any issues before they become costly or cause unsafe conditions in order to avoid downtime on ships.

Advantages of Processing with Edge Devices

Data complexity: If you’ve got a factory or manufacturing floor with hundreds of cameras and sensors in it then there’s just no way to send that information across the Internet to the cloud for processing — it’s going to overwhelm whatever kind of connection you have.

Latency: This is the time it takes for something to happen after a key event happened. It’s important in industrial and manufacturing because if there are sudden changes, such as a potential machine malfunction — then those cloud-based compute devices won’t be able to make decisions or predictions quick enough. Cloud processing is simply too slow. Predictive models running on the edge is the way to go.

Cost: The economics of cloud computing are getting better and cheaper all the time, but it still costs money. Edge Computing can reduce data consumption by sending less information to a server in a remote location, which saves energy as well as provides faster network speeds for users on competitive websites who do not have this advantage over them yet.

Reliability: The local processing of an asset-monitoring system means that it will be able to work even when connectivity goes down. Edge machine learning is great for both on- and off-grid industrial assets.

Privacy: With edge compute, sensitive live operational sensor data does not need to leave the facility or be shared with third parties.

Building a Predictive Maintenance Demonstration

Let’s look at how to assemble a solution that detects anomalous hardware vibrations.

Hardware Requirements

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  Akida Development Kit Raspberry Pi, keyboard, mouse, monitor

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  ADXL345

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  120mm case fan

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  Standoffs and screws — used are a #2-52 screw/nut to secure to fan