STMicroelectronics has introduced a two-axis digital inclinometer for use in industrial automation and structural-health monitoring.

STM-Inclinometer

It includes a programmable machine-learning core and 16 independent programmable finite state machines intended to save power for battery operation and reduce data transfers from edge sensors to the cloud.

Called IIS2ICLX, the device has selectable full-scale of ±0.5, ±1, ±2 or ±3g (g=gravity here, not grams), and provides outputs over I2C or SPI serial busses.

Internal compensation means stability within 0.075mg/°C, and claimed noise is 15μg/√Hz.

Sensitivity change over life is predicted to be -1.5 to +1.5%.

What is the difference between an inclinometer and an accelerometer, Elecronics Weekly asked ST – after all, they both measure acceleration?

“Generally speaking, while they share the same principle, they are not the same device with a new part number and spec,” an ST spokesman told EW. “The inclinometer is meant to measure a static deflection of the proof mass based on gravitational pull. Our inclinometers are designed to offer higher stability and repeatability over time and temperature range for greater accuracy. ST inclinometers and accelerometers share the same principle and are capacitive devices with a proof mass that deflects based on the forces acting upon it.”

Operation is over -40°C to +105°C, and sensitivity change with temperature over that range is is -0.012 to +0.012%/°C from 25°C.

Power is typically 420µA from a nominal 1.8V analogue rail (1.71 – 3.6V), and the I/O also needs its own 1.62 – 3.6V rail.

Digital output update rate can be varied from 12.5 to 883Hz.

To save  power by reducing the number of external reads, a 3kbyte FIFO is implemented to store: accelerometer, external sensor (up to four via internal sensor hub hardware), time stamp and temperature.

Programmable signal processing includes: high-pass and low-pass digital filters, a finite state machine and a machine learning core.

Of the latter, the data sheet explains: “The machine learning core logic allows identifying if a data pattern (for example motion, pressure, temperature, magnetic data) matches a user-defined set of classes. Typical examples of applications could be anomalous vibration recognition, complex movement or condition identification, activity detection. The learning core works on data patterns coming from the accelerometer, but it is also possible to connect and process external sensor data (from a gyroscope or additional external inclinometer/ accelerometer, temperature or pressure sensors) by using the sensor hub feature.”

Software libraries are available for sensor calibration and real-time computation of tilt angle in the X-Cube-Mems1 expansion software package for STM32Cube.

Packaging is 5 x 5 x 1.7mm ceramic-cavity LGA (not guaranteed hermetic).

“The combination of stability and repeatability, accuracy, and resolution make the IIS2ICLX particularly suited to industrial applications such as antenna pointing and monitoring, platform levelling, forklift and construction machines, levelling instruments, equipment installation and monitoring, and installation and sun tracking for solar panels, as well as Industry 4.0 applications such as robots and autonomous guided vehicles,” according to the company.