Ultra-Low Power Neuromorphic Front-End Chip for Vibration Analysis in IIoT
VibroSense, a tiny AI chip based on the Neuromorphic Analog Signal Processor, preprocesses vibration data at the sensor and addresses the major challenge of Industrial IoT: the amount of data to be transmitted, stored, and processed.
Machine Health monitoring
Sensor Raw Data Pre-Processing With Neural Networks Saves A Fortune!
Machine condition monitoring can be used to achieve the best possible upkeep of industrial equipment. It involves gathering and analyzing data from sensors such as vibration and acoustic. The machines being monitored are equipped with vibration sensors that collect data and transmit it to a local server or cloud for storage and processing. The vast amount of data generated by vibration sensors during operations makes condition monitoring costly due to the expenses associated with data transmission, storage, and processing.
VibroSense chip solves the problem by reducing the transmitted data volumes and making condition monitoring more accessible and cost-effective on a global scale.
1Raw data pre-processing on-sensor reduces data volumes by 4000 – 1000 times
2Transmitting only small data patterns supports narrow-bandwidth long-distance communications
3Processing significantly less data reduces OPEX and TCO of Predictive Maintenance solutions
Vibration-based condition monitoring is a fundamental Predictive Maintenance technique that is used to detect machine failures. By analyzing vibrations, it is possible to identify a range of machinery problems such as shaft unbalance and misalignment, bearing failures, gear wear, cracks, looseness, and more.
Vibrations can be used not only for monitoring relatively simple mechanisms, but also complex ones such as pumps, engines, or wind turbines.
However, vibration signals can be intricate, especially in complex machinery operating at varying speeds and loads. The presence of background and measurement noise further complicates signal analysis.
Leveraging neural networks for signal processing presents a very appealing approach. Neural networks can extract useful information even from very noisy signal, due to the non-linear way they process data. Some deep neural network architectures, as the utilized in VibroSense, prove to be exceptionally well-suited for addressing vibration monitoring challenges.
