tinyML EMEA – Alexander Timofeev: Data Pre-processing on Sensor Nodes for Predictive Maintenance

Data Pre-processing on Sensor Nodes for Predictive Maintenance
Alexander TIMOFEEV
Founder and Chief Executive Officer
Polyn.ai

Vibration-based condition monitoring is a fundamental Predictive Maintenance technique that is used to detect machine health conditions and predict failures. By analyzing vibrations, it is possible to identify a range of mechanical problems such as shaft unbalance and misalignment, bearing failures, gear wear, cracks, looseness, and more. Vibration sensors are typically attached to rotating equipment to measure the vibrations it generates. These sensors have a frequency bandwidth of up to 20KHz to ensure accurate prediction of mechanical failures. However, the high-frequency signals create especially large amounts of data to be processed by Machine Learning algorithms in continuous condition monitoring applications. Sending all this data collected on the sensor nodes to a central location for analysis would be more burdensome than beneficial. Reducing the amount of transmitted data would give latency improvement and save the transmission infrastructure costs as well as data processing and storage resources.

It is possible to replace high-volume vibration data with small patterns (embeddings) that are transmitted to the cloud instead. Despite the reduced size, the information contained in embeddings is still sufficient for reliable Predictive Maintenance.

To this end, we propose an innovative concept we call a Neuromorphic Front-End located next to the sensor, and a unique Neuromorphic Analog Signal Processing (NASP) technology for the implementation of a trained neural network in a tiny silicon chip made of analog circuitry elements. The role of the Neuromorphic Front-End chip is to extract useful information from raw sensor data, similar to the way biological sensory systems work.

The NASP technology utilizes a unique architecture comprising artificial neurons (nodes responsible for performing computations) and axons (connections between nodes with specific weights). Specifically, operational amplifiers are used to implement neurons, and a mask programmable resistors’ layer is used to implement axons and their weights. Such analog structure performs true parallel data processing without accessing memory and other excessive data traffic. This is the key to unprecedented energy efficiency, low latency, and 100% chip area utilization of NASP solutions.

The NASP approach involves the trained neural network modeling, verifying, and converting into the chip structure standard files that any foundry can use for chip manufacturing. This is different from costly attempts to accommodate a neural network on a general-purpose digital chip. The result is an application-specific analog inference engine tailored for the task and complemented with a fully flexible digital layer responsible for classification.

NASP is capable to process the raw data directly on the sensor node with high precision, extracting vibration embeddings, and reducing the data flow by 1000 times.

Power consumption is a critical factor in battery-powered Industrial IoT applications, with data transmission accounting for 85-99% of the total consumption in wireless sensors. Thousandfold data reduction by NASP answers this challenge enabling LPWA (low power wide area) data communication. NASP saves the sensor node power budget due to its ultra-low power consumption of only 100µW on always-on operations. NASP Neuromorphic Front-End chips support the widespread use of wireless and energy-harvesting solutions. The use of a Neuromorphic Front-End enables deployments in previously inaccessible remote and mobile locations. It also simplifies the entire system and reduces associated operational and capital expenses.

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