Industries

Predictive Maintenance
(PdM)

Predictive maintenance is a critical element of the Industry 4.0 initiative for smart factories. By using smart sensors to monitor the current health and conditions of industrial equipment, with enough data, factories can predict when and where issues will occur in the future.

With Qeexo AutoML

Example Sensor Applications

The sensor modules used in PdM typically contain an MCU and Bluetooth/BLE/Wi-Fi or other connectivity option along with sensors that are ideal for detecting various performance issues and anomalies.

  • Accelerometer and gyroscope-based vibration sensors monitor the movement of the machine and can detect when it is not operating optimally.
  • Acoustic emission sensors are high frequency microphones that can listen to a bearing falling apart.
  • Temperature and humidity sensors observe the ambient environment.
  • A microphone listens to the machine to detect anomalies.

The Qeexo AutoML Advantage

 

Minor differences in the hardware setup and environment, such as mounting the sensors on a different surface material/location or the ambient temperature, can affect the accuracy of a machine learning model. For best performance, each individual machine needs to be equipped with a unique model that is periodically updated and improved.

 

With an intuitive UI, Qeexo AutoML allows for onsite data collection, automated model development, and swift library deployment so users can find the best model for each unique use case quickly and efficiently.

Structural Health Monitoring
(SHM)

Governments all over the world are investing in machine learning and sensors attached to bridges and other structures to monitor their “health”. In Japan, for example, regular earthquakes and tremors increase the fatigue of bridges, railroads, roads, and buildings, which need to be monitored in the interest of public safety.

With Qeexo AutoML

Example Sensor Applications

Sensors commonly used in SHM include high precision, low drift/bias accelerometers, strain gauges, displacement transducers, level sensing stations, anemometers, temperature sensors, and dynamic weight-in-motion sensors. They measure everything from tarmac temperature and strains in structural members to wind speed and the deflection and rotation of the cables, and any movement of bridge decks and towers.

These sensors are often combined with a low-power MCU and a low data bandwidth connectivity technology like LoRa.

The Qeexo AutoML Advantage

 

Often, thousands of sensors are required to monitor a single structure. Traditionally, all of the raw sensor data are sent to a remote location for processing. Machine learning models built with Qeexo AutoML are optimized to run locally on MCUs, so decisions can now be made on-device.  Only useful information such as alerts, not the raw data, are relayed to the monitoring station.

 

In addition, due to the remoteness of the sensors attached to structures in SHM, these sensors are typically required to run on limited battery power for extended periods of time. Qeexo AutoML produces extremely efficient models with low power consumption, thus extending the battery life of these devices.

Smart Home/Appliances

Intelligent appliances and other smart devices around the home have gained popularity in recent years. Most of these are equipped with sensors but only using very primitive algorithms and logic. Machine learning can further increase the usability and functionality of these devices, making our daily lives more convenient.

With Qeexo AutoML

Example Sensor Applications

  • A smart stovetop with microphone could detect when a frying pan is burnt by listening to the sizzling sound and turning down the heat when needed, or detect when the water is boiling over by recognizing the unique sound signature.
  • Motion sensors in a smart washer or dryer could detect a load imbalance, instructing the machine to adjust itself automatically instead of stopping or damaging the equipment. It could also monitor the machine’s vibration signature to detect sub-optimal performance and alert the owner that maintenance is required.
  • Air quality and gas sensors can be combined with the thermostat to monitor the safety and comfort level of the ambient air at home, automatically turning on and off the air purifier, humidifier, heat and air conditioning as necessary.

The Qeexo AutoML Advantage

 

Compared to traditional, threshold-based logic, devices equipped with machine learning can better make sense of the sensor data and make these appliances around the consumers’ homes even smarter.

 

In addition, since models built with Qeexo AutoML runs on-device, consumers’ data is not sent to the cloud, preserving their privacy and saving data and infrastructure costs.

Wearable IoT Devices

Due to their compact form factor, wearable devices are highly constrained in processing power, memory size, and battery life. Balancing performance with battery life is a constant struggle for device manufacturers.

With Qeexo AutoML

Example Sensor Applications

  • In wrist-worn wearable devices, motion sensors are used for the “bring-to-see” gesture as well as for activity classification (e.g. walking, running, biking) and contextual awareness (whether wearer is inside a car or in an elevator).
  • As the world’s population ages, many countries such as US and Japan are investing heavily in tracking the activities and health of their elderly. Whether at home or in an assisted living facility, seniors can be cared for 24/7 with a smartwatch or a medallion worn around the neck to detect movement and accidental falls.
  • Smart farming seeks to employ advanced technology to improve traditional farming and agricultural techniques in an effort to raise profit, combat climate change, address population growth, and increase sustainability. Sensors are often used for soil scanning, and water, light, humidity, and temperature management. Tracking the cattle’s estrous cycle and activities is vital in maximizing profit from the animals.
  • Pet owners are spending money on wearables with motion-sensing and GPS capabilities to track their pets’ location and activities to keep them happier, well-rounded, safe, and healthy.

The Qeexo AutoML Advantage

 

Often, wearables in the market are either extremely expensive or not very smart and needs to be connected to your smartphone for full functionality. Powered by a mid-to-low-tier MCU, the processing power and memory space available to wearables are limited, making developing TinyML on these constrained environments very difficult.

 

Optimized for the Edge, Qeexo AutoML produces lightweight models that are powerful but small in size and uses very little power, giving your devices high intelligence without the high costs.

Try Qeexo AutoML

Register for a free evaluation or other SaaS options.

Try Qeexo AutoML

Register for a free evaluation or other SaaS options.