Predictive Maintenance
Sensor Parameters
for
Predictive
Maintenance
Use Cases
Vibration Monitoring
Demo Set-upReference Design
Placeholder for picture
Distinctive Sensor Parameters for Predictive Maintenance
Accelerometer & Microphone
Machin
e c
onditio
n
Time
Conditions
start to
change
Vibration
Power
Smoke
Failure
minutes
days
months
weeks
UltraSound
Noise
Heat
Ac
ce
lero
me
ter
Fo
r vib
rati
on
me
as
ure
me
nts
Signal Bandwidth. Frequency Response & FilteringDifferent defects/wears shows up at different frequencies and should
be captured without ambiguity
Noise DensityLower Noise allows to identify earlier defects and wears
Operating Temp RangeSensor should match the operating condition of the monitored
equipment
Number of axis3 axis allows to monitor all kind of defects/wears (imbalance,
misalignment, bearings, etc.)
Power ConsumptionImportant merit figure for battery operated Sensor nodes
Output InterfaceDigital output is the optimal solution for complexity, cost and reliability
Mic
rop
ho
ne
Fo
r U
ltra
so
un
d
& N
ois
e s
en
sin
g Operative Bandwidth Microphone working in standard audio bandwidth as well as in the
ultrasonic domain
Dynamic RangeAOP (acoustic overload pressure) is required to keep sensing
despite of the presence of strong environmental sound emissions
Output InterfaceBoth analog and digital for wide compatibility with processing units
Operating RangeWide temperature and humidity ranges to cover wide set of use
cases
AccuracyHigh reading accuracy across whole temperature range
Output InterfaceDigital output is the optimal solution for complexity, cost and
reliability
Power ConsumptionImportant merit figure for battery operated Sensor nodes
Environmental sensorsDistinctive Sensor Parameters for conditions monitoring
Pre
ss
ure
se
ns
or
Signal full scaleThe Industrial market covers a broad scope of applications that
range from very low vacuum
Temperature compensationto guarantee sensor’s accuracy over the whole operating
temperature range
Output InterfaceDigital output is the optimal solution for complexity, cost and
reliability
Water and harsh environment resistance featureCompatible with large set of environments where water and
chemicals are present
Te
mp
era
ture
an
d h
um
idit
y
Symptoms According to Audio FrequencyStandard audio vs ultrasonic
Standard Audio Bandwidth Ultra-Sonic
Audible noise
(CbM, Preventive Maintenance)Predictive
Maintenance
Post processing analysis
Ultrasonic frequencies to detect
and classify leaks
Motor Control Use CasePredictive monitoring through audio: Minimize damage with early detection
• Most common maintenance applications that could be applied at your plant today
• Air Leak Detection of compressed air equipment
• Vibration monitor: All rotating equipment produces frictional forces with high frequency ultrasonic signatures which are often masked by ambient plant noise and low frequency vibrations
• Compressor Valve Inspections
• Acoustic Lubrication
• Heat Exchanger and Condenser Leaks
• Hydraulic Systems
• Pump Cavitation
Good bearing Failing bearing
Amplitude and
Frequencies matter
Environmental Sensing Use Cases
Condition-based monitoring examples for Smart Industry
Environmental sensors play critical roles in process and quality
Measure operating temperatures, It’s useful
for detecting loose or improperly terminated
electrical connections, overloading, defective
contacts, phase imbalances and other
electrical problems
Pressure measurements for "air
management” systems, which monitor the
performance indicators and the different stages
of the air compressors connected to the
compressed-air supply grid
Largely adopted in HVAC system control
water vapor level or to help in regulating
parameters such as air temperature and
blowing speed
Predictive Maintenance Demo
Vibration monitoring with cost-effective MEMS accelerometer over IO-Link industrial connectivity. The
STM32F4 performs local real-time frequency/time domain analysis for early motor failure detection
• Demo Overview
• #1: Inertial and environmental sensors connected via IO-Link are used
to detect the status of the motor and thus to predict possible failures.
• #2 The demo performs frequency and time domain analysis of a good
motor vs. one unbalanced. Results are shown on a screen thanks a
GUI.
• #3 The STEVAL-IDP005V1 (ISM330DLC, LPS22HB, HTS221,
STM32F469, L6362A) is fixed on the motors and is connected via IO-
Link to the STEVAL-IDP004V1 (L6360, STM32F2), acting as Master IO-
Link. The two motors are driven using one dual motor control board
STEVAL-IHM042V1 (L6230, STM32F3).
Predictive Maintenance Demo Setup
FFT
RMS
Peak
STEVAL-IDP005V1
STEVAL for motors drive
STEVAL IO-Link Master board
good unbalanced
knob for speed setting
GUI Details 1/2
Time Domain
Analysis
Frequency Domain
Monitoring
Good motor behaviour
Motor with anomalies
Motors
speed frequency
GUI Details 2/2
Environmental
equipment
monitoring
Local Processing
• STM32F469AI 32-bit ARM Cortex-M4
microcontroller
Condition Monitoring Through IO-LinkReference Platform
EquipmentUse c
as
es
Motors Environment
STEVAL-IDP005V1
Release: Q2’18
Sensing
Processing
Connectivity
• Optimized form factor for industrial M12 connector
• Embedded algorithm for sensors data analysis, detecting
anomalies like unbalance, misalignment, or bad equipment
condition
• Logging of worst working condition events
Main Features
Vibration and Environmental
• ISM330DLC 6-Axis digital MEMS axel and gyro
• MP34DT05-A Microphone
• LPS22HB MEMS Pressure sensor
• HTS221 Humidity & Temperature Sensor
• STTS751 Digital temperature sensor
Wired
• L6362A IO-Link communication transceiver
device IC
STEVAL-IDP005V1Reference Eval Platform
• Reference design for predictive maintenance with vibration,
acoustic and environmental monitoring (P, RH, T)
• ARM® Cortex®-M4 core for signal processing and analysis
• Signal conditioning algorithms include:
• Accelerometer data high-pass filter
• IIR exponential filter
• Frequency domain analysis Accelerometer and Speed on three
axes
• Programmable FFT averaging
• Programmable windowing (Flat Top, Hanning, Hamming)
• Programmable overlapping (up to 75%)
• Alarm settings on programmable thresholds
• Time domain analysis (axel on three axes)
• Speed RMS moving average
• Accelerometer Peak to peak
• Data EEPROM Storage (i.e. worst working condition events )
• Pressure, Humidity and temperature environmental monitoring
• Microphone FFT for acoustic emission analysis
• IO-Link connectivity*
• Expansion connector (ADC, I2C, GPIO)
• M12 circular housing compatible* Stack can be hosted by microcontroller but it is not included in STEVAL package
STEVAL-IDP005V1 – Reference Design Features
Next to release: Q2’18
IO-Link Evaluation Board 4-port Master
• Designed according with IEC and EN standards to address factory automation segment, in particular:
• IEC61000/2-4
• EN60947-5-2
• Several HW interface available on master node to provide more flexibility for data exchange
• IO-Link PHY
• RS485
• CAN
• USB
STEVAL-IDP004V1* – IO-Link 4-port Master
(*) already available on stock
STEVAL-IDP005V1 communication based on
Master Board
STEVAL-IDP004V1
STEVAL-IDP005V1
Axel spectrum
RMS
Axel Peak
P, T, H parameters
STEVAL-IHM042V1Motor Control
STEVAL-IHM042V1* –Low Voltage dual motor control demonstration platform
Key Features
• Highly compact dual 3-phase motor control design
• Two L6230 monolithic power stages (PowerSO package), featuring overcurrent and thermal protection
• STM32F303 microcontroller capable of simultaneous driving field-oriented control (FOC) of two PMSM motors
• Designed for sensored and sensorless motor control
• Input voltage from 8 V to 48 V
• Up to 10 W continuous output power for each motor
• 3- or 1-shunt current sensing topology for each drive
• On-board STLink
• USB interface
• Compatible with ST PMSM FOC firmware library and ST MC Workbench
(*) already available on stock
Top Related