Key Product Benefits
ISM330DLC -inertial measurement unit
This iNEMO inertial measurement unit is well suited to vibration monitoring in industrial environments. It is a highly robust package that offers comparable accuracy with piezoelectric sensors and high speed signal transmission along dedicated paths, as well as ultra-low power consumption.
STM32F4 - MCU with DSP capability
The STM32F4 series microcontroller is chosen for its digital signal processing capabilities and clock speeds in order to manage the very high data rates streaming in from the vibration and environmental sensors.
LPS22HB and HTS221 - environmental sensors
The LPSSHB digital pressure and HTS221 relative humidity and temperature sensors complete the data requirements for a comprehensive condition monitoring scenario.
STM32MP157 - MPU as gateway node
The dual core STM32MP157 microprocessor provides a full computing environment hosting the applications to manage data routed from multiple monitoring nodes. Here, the data can be stored, processed and queued while the same microprocessor manages connectivity handshakes and transmission schedules with centralized condition monitoring and predictive maintenance systems.
- Vibration monitoring data in the form of vibration speed (RMS), peak acceleration, and FFTs performed by STM32 MCU core on data acquired from ST industrial accelerometer.
- Temperature, humidity and pressure data from ST environmental sensors.
- Condition monitoring software support demonstrating Edge node processing in communication with a Cloud application via a secure gateway.
- End-to-end communication framework allowing Condition Monitoring platform to develop into a Predictive Maintenance solution.
- Further processing potential on Edge node with AWS IoT Greengrass and Lambda functions.
- Cloud Dashboard to register and provision the devices, configure a gateway for Edge processing, assign a gateway to a group of devices, analyze real time and historical data, and set thresholds to trigger alerts for specific conditions.
The architecture we propose is based on an STEVAL-IDP004V1 master board and up to four STEVAL-BFA001V2B* smart sensor nodes, which export the following condition monitoring data over a serial protocol:
- environmental pressure, humidity, and temperature data
- time and frequency domain vibration data from the embedded accelerometer, processed by STM32F4 microcontroller
The Edge node collects environmental data and FFT data from accelerometers processed by the STEVAL-BFA001V2B* kit, which is then sent via MQTT over Ethernet or Wi-Fi to the DSH-PREDMNT dashboard based on the AWS infrastructure.
The data is collected and further processed in an Edge gateway consisting of an STM32MP157C-DK2 kit running X-LINUX- PREDMNT software, which includes the AWS IoT Greengrass service.
The DSH-PREDMNT dashboard completes the journey with a web-based tool to manage device provisioning, configuration, data injection and analysis, and simple thresholds for anomaly detection from a centralized Cloud service.
The AWS IoT Greengrass Edge Computing service allows local computation of Lambda functions on Edge gateway nodes with the same logic available on the Cloud to ensure continuity even when connection to the Cloud is unavailable; shadow devices on the Cloud are automatically synchronized with the Edge nodes as soon as connection is re-established.
* STEVAL-BFA001V1B is no longer recommended for new design
|DSH-PREDMNT||Cloud based web application for condition monitoring and predictive maintenance||Active||Go to site|
|STSW-BFA001V1||Software package for STEVAL-BFA001V1B||Active|
|STSW-BFA001V2||Software package for STEVAL- BFA001V2B multi-sensor development kit for condition monitoring and predictive maintenance||Active|
|STSW-IDP4PREDMNT||Predictive maintenance evaluation kit firmware||Active|
|STM32 MPU OpenSTLinux Expansion Pack for Predictive Maintenance application||STM32 MPU OpenSTLinux Expansion Pack for Predictive Maintenance applications||Active||Go to site|
All Evaluation Features
- Main supply voltage: 18 V - 32 V
- IO-Link device stack v1.1 protocol and IO-Link Device Descriptor (IODD) for all measurements included (provided by TEConcept GmbH)
- IMicrophone algorithms for:
- PDM to PCM
- Sound pressure level (SPL)
- Audio FFT
- Complete set of firmware demo examples based on 3D accelerometer library with advanced frequency and time domain signal processing for predictive maintenance, including:
- Programmable FFT size (256, 512, 1024, 2048), overlapping and averaging
- Programmable windowing (Flat Top, Hanning, Hamming, Rectangular)
- Speed RMS moving average, acceleration max. peak
- Programmable threshold for warning and alarm conditions in spectral band
- M12 industrial connector
- SWD connector for debugging and programming capability
- Reset button
- Expansion connector with GPIO, ADC, I2C bus
- Designed to meet IEC industrial standard requirements
- Master IO-Link stack embedded with read out protection
- Limitation of time of use (10000 minutes)
- Fully compatible with all IO-Link devices
- Main supply voltage 32 V maximum
- 4 L6360 IO-Link master transceiver ICs
- RS-485 serial interface
- CAN serial interface
- USB interface
- DC-DC converter
- On-board reverse polarity protection
- Designed to meet IEC requirement for industrial standards
- RoHS and WEEE compliant
- 4-Gbit DDR3L, 16 bits, 533 MHz
- 1-Gbps Ethernet (RGMII) compliant with IEEE-802.3ab
- USB OTG HS
- Audio codec
- 4 user LEDs
- 2 user and reset push-buttons, 1 wake-up button
- V / 3 A USB Type-CTM power supply input (not provided)
- Board connectors: Ethernet RJ454 × USB Host Type-AUSB Type-CTM DRPMIPI DSISMHDMI®Stereo headset jack including analog microphone input microSDTM cardGPIO expansion connector (Raspberry Pi® shields capability)ARDUINO® Uno V3 expansion connectors
- On-board ST-LINK/V2-1 debugger/programmer with USB re-enumeration capability: Virtual COM port and debug port
- 4" TFT 480×800 pixels with LED backlight, MIPI DSISM interface, and capacitive touch panel
- Wi-Fi® 802.11b/g/n
- Bluetooth® Low Energy 4.1
MEMS and Sensors
|07 Feb 2020||
07 Feb 2020
|02 Jul 2020||
02 Jul 2020
|08 Oct 2020||
08 Oct 2020
|12 Jun 2020||
12 Jun 2020