SL-PREDMNT-E2C
Data brief
Edge processing enabling Condition Monitoring and Predictive Maintenance:
quick start for end-to-end architecture based on wired Smart Sensor Nodes and
Gateway
STM32MP157C-DK2 rev. C01
X-LINUX-PREDMNT
PredMnt application
WireST
SDK
EdgeST
SDK
IoT Cloud Application
DSH-PREDMNT dashboard
STEVAL-IDP004V1
Serial
Interface
AWS IoT Greengrass
Predictive Maintenance
Cloud application
STEVAL-BFA001V1B
sensor nodes
Value proposition and benefits
ST provides a comprehensive framework for users to develop and test Condition Monitoring and Predictive Maintenance
solutions based on vibration and environmental data streams.
We provide users with a quick start environment for Proof of Concept of industrial solutions connecting multiple sensor nodes to
a central data lake such as a Cloud service.
Critical vibration data is processed locally on
STEVAL-BFA001V1B
sensor nodes by an STM32 microcontroller, which outputs
frequency and time domain data, as well as temperature, pressure and humidity data. The data from up to four sensor nodes
with IO-Link transceivers (IO-Link stack not included) is then routed through an IO-Link master to an Edge gateway node
(STM32MP157C-DK2 Discovery Kit), where all the data is consolidated and further processed by server-based or cloud-based
elaboration and connectivity software.
In order to expose the potential of a cloud-based solution, we provide a Predictive Maintenance Dashboard application from
which Edge gateway nodes running the AWS IoT Greengrass service and AWS IoT core can be provisioned, so that condition
monitoring sensor data can be plotted and triggers can be configured as part of your end-to-end Predictive Maintenance
solution.
Features
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•
•
•
•
•
Vibration monitoring data in the form of vibration speed (RMS), peak acceleration, and FFTs performed by STM32 core on
data acquired from ST industrial accelerometer.
Temperature, humidity and pressure data from ST environmental sensors.
Condition monitoring example 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 particular equipment
conditions.
Free usage terms for a limited number of sensors and gateways, and for a limited time, as part of the DSH-PREDMNT
Cloud application user license agreement.
Based on STM32Cube and STM32OpenSTLinux expansion packages.
Serverless deployment of the Dashboard application in user account through Cloud Formation tool.
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For further information contact your local STMicroelectronics sales office.
www.st.com
SL-PREDMNT-E2C
Description
The Predictive Maintenance Platform (PMP) is a condition monitoring application for the operating conditions of industrial
equipment.
All manufacturing equipment with moving parts are subject to degradation which require servicing or component replacement,
but traditional maintenance approaches based on set schedules ignore actual equipment condition. In Condition-based
monitoring, maintenance is instead scheduled according to the estimated condition of the machine from inspection or from
sensor data.
Predictive Maintenance builds on condition monitoring by feeding sensor data into dynamic predictive models for failure modes
in an attempt to foresee maintenance requirements as far into the future as can be deemed practical. This can translate into
more efficient maintenance planning, less machine down time and longer operating life through investment in system
intelligence and other ERP data like equipment life cycle.
The process of evolving from Condition Monitoring to Predictive Maintenance begins with establishing information criteria and
building appropriate systems to sense and deliver the data, followed by more intricate phases involving the optimization of
thresholds through experience and historical data, and finally the implementation of predictive models able to provide accurate
forecasts of the future condition of manufacturing equipment.
Figure 1.
Pathway from Condition Monitoring to Predictive Maintenance
Time and frequency analyses of vibration data is especially useful for the identification of anomalies. Different analytical
techniques can be used, which can include deep learning and AI technologies.
With respect to
Figure 1,
this solution is designed to get users to step three, in order to gain familiarity with the environment and
equipment in which vibration or environmental analysis may be performed.
The architecture we propose is based on an
STEVAL-IDP004V1
master board and up to four
STEVAL-BFA001V1B
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
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Figure 2.
Condition monitoring and Edge to Cloud: from sensors to gateway to cloud dashboard
The Edge node collects environmental data and FFT data from accelerometers processed by the STEVAL-BFA001V1B kit, which is then sent
via MQTT over Ethernet or Wi-Fi to the DSH-PREDMNT dashboard based on the AWS infrastructure.
STM32MP157C-DK2 rev. C01
X-LINUX-PREDMNT
PredMnt application
WireST
SDK
EdgeST
SDK
IoT Cloud Application
DSH-PREDMNT dashboard
STEVAL-IDP004V1
Serial
Interface
AWS IoT Greengrass
Predictive Maintenance
Cloud application
STEVAL-BFA001V1B
sensor nodes
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 reestablished.
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Hardware components, software and core products of Predictive Maintenance solution
1
Hardware components, software and core products of Predictive
Maintenance solution
Each hardware can be used in different ways respect what is here described. You can access the www.st.com
related page to get more information.
For example, the smart sensor node can be used in a standalone configuration with his firmware to be integrated
in other use cases.
As well the Smart Sensor Node and the Master offer the capabilities to be integrated with other gateways and
concentrators.
Table 1.
Key components of Predictive Maintenance solution
Component
Smart Sensor Node
Master
Gateway
DASHBOARD
Hardware
STEVAL-BFA001V1B
STEVAL-IDP004V1
STM32MP157C-DK2
Firmware / Software
STSW-BFA001V1
STSW-IDP4PREDMNT
X-LINUX-PREDMNT
DSH-PREDMNT
1.1
STEVAL-BFA001V1B with STSW-BFA001V1
Figure 3.
STEVAL-BFA001V1B
The
STEVAL-BFA001V1B
is an industrial reference design kit designed for condition monitoring (CM) and
predictive maintenance (PdM).
The hardware consists of an industrial sensor board, an adapter for the ST-LINK/V2-1 programming and
debugging tool (STEVAL-UKI001V1), a 0.050” 10-pin flat cable, a 4-pole cable mount connector plug with male
contacts and an M12 female connector with a 2 m cable. The firmware package
STSW-BFA001V1
includes
dedicated algorithms for advanced time and frequency domain signal processing and analysis of the 3D digital
accelerometer with 3 kHz flat bandwidth.
The firmware runs on the high performance STM32F469AI, ARM
®
Cortex
®
-M4, 32-bit microcontroller and the
sensor data analysis results are sent via wired connectivity based on IO-Link device transceiver (IO-Link stack
protocol not included).
A dedicated version for the solution is provided in binary format.
Main products:
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•
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32-bit ARM
®
Cortex
®
-M4 core for signal processing and analysis (STM32F469AI)
–
Precompiled binary: STSW-BFA1PREDMNT.zip based on STM32Cube
iNEMO 6DoF (ISM330DLC)
Absolute digital pressure sensor (LPS22HB)
Relative humidity and temperature sensors (HTS221)
Digital microphone sensors (MP34DT05-A)
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STEVAL-IDP004V1/STEVAL-IDP004V2 IO-Link master with STSW-IDP4PREDMNT
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IO-Link PHY device (L6362A)
EEPROM (M95M01-DF) for data storage
Step-down switching regulator and LDO regulator (L6984 and
LDK220)
1.2
STEVAL-IDP004V1/STEVAL-IDP004V2 IO-Link master with STSW-
IDP4PREDMNT
Figure 4.
STEVAL-IDP004V1/STEVAL-IDP004V2 IO-Link master
The
STEVAL-IDP004V1/STEVAL-IDP004V2
IO-Link master evaluation board with STM32 microcontroller has four
L6360
ICs. Communication with the ICs is via I²C in master mode and is managed by the
STM32F205RB
MCU.
Each L6360 has its own address and shares the bus with the other devices.
The IO-Linik master evaluation board is developed to create a multi-port master based on serial asynchronous
communication to support the IO-Link protocol. Each node is equipped with an industrial M12 connector (as
required by the standard) for connection with a single slave node using a 20 meter cable. Beyond the IO-Link
connection, the board includes RS-485 bus, CAN bus and USB hardware interfaces.
The layout is designed to meet the requirements for IEC61000-4-2/4/5 for the industrial sector.
The
STSW-IDP4PREDMNT
firmware implements asynchronous serial communication to manage data exchange
between sensor nodes and edge nodes. Both communication protocols have been customized to better fit the
application needs, no standardized protocol (like IO-Link stack) has been included.
A set of commands have been implemented, to enable the communication between gateway and smart sensor
node, to transfer environmental data and vibration data (time domain and frequency domain).
This binary for master node is compatible with STEVAL-IDP004V1 as well as STEVAL-IDP004V2.
Main products:
•
4x
L6360
IO-Link master devices
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RS-485 serial interface
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CAN serial interface
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USB interface
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DC-DC converter
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Precompiled binary: STSW-IDP4PREDMNT.zip based on STM32Cube
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