(833) 881-5505 Request free consultation

Sensors used in BigData for enterprise predictive maintenance

Appendix

A look into how some important Sensors are used in BigData for enterprise predictive maintenance.

In this Appendix we give below a table containing some important Sensors used in big data for enterprise predictive maintenance


But before that, what are these sensors?

Sensors are used to record data. This data can be used for analysis, prediction, maintenance and also measuring performance. Here we discuss the use of data sources used in predictive maintenance.


And what is enterprise predictive maintenance?

It is a data-driven approach that uses advanced analytics and machine learning algorithms. The aim is to optimize maintenance schedules and prevent equipment failures in large-scale industrial operations.


And how are sensors important in enterprise predictive maintenance?

Enterprise predictive maintenance involves collecting and analyzing data from sensors and other sources.


How do they work together?

The data received is used to identify patterns and trends that indicate impending equipment failures or performance degradation.


Finally, where are they used?

Predictive maintenance can be applied to various types of equipment. Examples include turbines, pumps, generators, etc. It is used in many other complex machinery used in manufacturing, energy, transportation, and other industries.


Some more insight into data sources.

There are a variety of data sources and sensors used for the predictive maintenance as part of operational efficiency improvement in enterprises.:

  • Data from such sources can be used to analyze and extract valuable information about the condition and performance of equipment

  • Such analysis could identify patterns and trends that indicate when a machine is likely to perform less or even fail

  • Based on such analysis maintenance teams can schedule proactive maintenance activities to prevent equipment failure, reduce downtime, and increase efficiency.


What WNPL can do.

WNPL can work with most of such data sources to help enterprises improve their operational efficiency and improve the bottom line. Benefits of the use of these sensors include

  • Provide upper management with a clear view of the current situation of machines and processes

  • Analyze efficiency of equipment or process being monitored

  • Predict failure using the big data received from such sensors

  • Suggest maintenance schedules & routines

  • Improve overall efficiency

  • Reduce downtime


Some such sensors WNPL can work with are listed below.


Sensors and Data Sources

* Note: The "other benefits" column gives any other benefits than the common benefits of analyzing these data:

  1. predict failure

  2. optimize maintenance

  3. reduce downtime



Name of Data Source

Measurement

Other Benefits*

Accelerometer data

  • acceleration of machine components


Ambient temperature data

  • temperature of the environment around the machine


Asset tracking data

  • Monitor the location and movement of equipment using tracking devices


Audio data

  • Monitoring and analyzing sounds emitted by equipment


Augmented reality data

  • Using augmented reality technology to provide maintenance technicians with real-time equipment data and guidance


Barcode data

  • Collecting data from barcodes on equipment and parts

  • Track inventory

Barometer data

  • atmospheric pressure around the machine

  • Predict/prevent failures due to environmental factors

Chemical sensor data

  • concentration of chemicals in fluids or gases


Computer-aided design (CAD) data

  • Analyzing CAD models to identify potential design flaws or errors. Usually this is used aling with data from other sensors


Computerized maintenance management system (CMMS) data

  • Collecting data from a CMMS to track maintenance activities and equipment performance


Conductivity sensor data

  • electrical conductivity of fluids within a machine


Current sensor data

  • electrical current flowing through machine components


Customer feedback data

  • Collecting feedback from customers regarding equipment performance


Cybersecurity data

  • Monitoring system logs and network traffic for suspicious activity

  • Prevent cyber attacks that could lead to equipment failure/downtime

Drones data

  • Sensor measurements such as temperature, vibration, and pressure, along with system logs


Electrical data

  • electrical properties such as voltage or current in equipment


Electrical usage data

  • Monitoring the electrical usage of equipment to detect anomalies or changes in performance


Electronic control module (ECM) data

  • Monitor the performance of engines and other powertrain components


Electronic control unit (ECU) data

  • Collecting data from the ECU in vehicles or machinery to monitor performance and detect issues


Energy consumption data

  • energy consumption of equipment over time

  • Reduce energy consumption, also predict failures

Energy usage data

  • Monitoring the energy usage of equipment to detect anomalies or changes in performance


Environmental data

  • Monitoring environmental factors such as temperature, humidity, and air quality that may affect equipment performance


Environmental sensor data

  • environmental factors that can affect equipment performance, such as temperature, humidity, or air quality


Equipment manuals

  • Analysing repair and maintenance manuals for insights. Usually AI is used assist this along with data from other sensors

  • Better understand the behavior of specific equipment

Failure history data

  • Collecting and analyzing data on equipment failures to identify patterns and improve maintenance practices


Flow meter data

  • flow rate of fluids or gases through equipment


Flow sensor data

  • flow rate of fluids within a machine


Fluid analysis data

  • Analyzing the properties of fluids used in equipment, such as oil or coolant


Fluid analysis data

  • Analyzing fluids such as oil or coolant to detect contamination or degradation


Fuel consumption data

  • Monitoring the fuel consumption of equipment to detect anomalies or changes in performance


Gas chromatography data

  • composition of gases within a machine


Gas sensor data

  • the presence and concentration of gases in equipment or the environment


Geographic information system (GIS) data

  • Collecting geographic data, such as location or terrain information, to analyze equipment performance


GPS data

  • Tracking the location and movement of vehicles and equipment


Hall-effect sensor data

  • magnetic fields within a machine


Heat map data

  • Analyzing thermal images of equipment to identify hot spots or temperature changes


Humidity data

  • moisture content of the air or environment surrounding equipment


Humidity sensor data

  • relative humidity within a machine


Image analysis data

  • Analyzing images or video footage of equipment to detect visual anomalies or defects


Impedance-based sensor data

  • electrical impedance of machine components


Industrial robots data

  • Sensor measurements such as temperature, vibration, and pressure, along with system logs


Infrared imaging data

  • Analyzing thermal images of equipment to detect hot spots or temperature changes


Infrared thermography data

  • Using infrared technology to measure temperature variations in equipment


Laser displacement sensor data

  • distance between machine components


Laser scanning data

  • Using laser scanning to create 3D models of equipment and detect defects


Level sensor data

  • level of fluids within a machine


Lidar data

  • distance between the machine and other objects in the environment using laser light


Load cell data

  • weight or force applied to equipment or materials


Load data

  • load or weight on equipment or structures


Machine log data

  • Record operational events and errors


Machine vision data

  • Capture and analyze images of equipment and components, using machine vision technology

  • Keeps visual record of defects and wear

Magnetic resonance imaging (MRI) data

  • Using MRI to detect internal damage or wear in machinery


Magnetic sensor data

  • Magnetic fields generated by machine components


Magnetic stripe reader data

  • Read data from magnetic stripes on equipment or materials


Maintenance logs

  • Record the history of maintenance and repairs


Maintenance records

  • Collecting and analyzing maintenance records to identify trends and improve maintenance practices


Manufacturing execution system (MES) data

  • Production processes and machine utilization


Microphone data

  • Record machine sounds and detect anomalies


Mobile device data

  • Collecting data from employee mobile devices such as GPS location, movement, and usage


Motion sensor data

  • Motion or movement of equipment


Noise level data

  • Noise levels generated by equipment


Performance data

  • Collecting data on equipment performance, such as throughput or efficiency


PLC data

  • machine performance and operations


Power quality data

  • the quality and consistency of power supplied to equipment


Pressure sensor data

  • pressure of fluids or gases within a machine


Process data

  • Data on the processes and operations performed by equipment


Radar data

  • distance between the machine and other objects in the environment

  • Prevent collisions
    Optimize machine performance

Remote monitoring data

  • Collecting data from sensors or monitoring devices installed on equipment to monitor performance and detect issues remotely

  • Helps detect potential issues early and prevent equipment failure

RFID data

  • Using RFID technology

  • Track the location and movement of equipment and components

  • Prevent loss or theft of equipment

  • Reduce inventory loss

SCADA data

  • Monitor system performance and operations


Social media data

  • Monitoring social media platforms for mentions of equipment or maintenance issues

  • Improve customer satisfaction

Spectroscopy data

  • Spectral properties of machine components

  • Identify potential issues


Spectroscopy sensor data

  • chemical composition of fluids or gases within a machine


Strain gauge data

  • deformation of machine components under stress


Tachometer data

  • rotational speed of machine components


Telemetry data

  • Transmit machine data wirelessly

  • Provides real-time data for remote monitoring and predictive maintenance

Temperature data

  • the temperature of equipment or its environment


Thermal imaging data

  • temperature of machine components


Time series data

  • Collecting and analyzing historical data over time to identify patterns


Torque sensor data

  • Torque or rotational force applied to equipment


Ultrasonic data

  • thickness and structural integrity of machine components.

  • Detect cracks or defects in equipment


Ultraviolet (UV) sensor data

  • amount of UV light emitted by a machine


Vibration data

  • vibration of equipment

  • Detect anomalies or changes in performance


Video surveillance data

  • video footage from cameras

  • Identify potential safety hazards,

  • prevent theft,

  • optimize maintenance schedules

Voltage sensor data

  • electrical voltage in machine components

  • Identify potential issues, predict failures due to electrical issues

Water quality data

  • Water quality in equipment such as boilers or cooling towers


Wearable sensor data

  • Physiological parameters of workers such as heart rate, body temperature, and motion

  • Ensure worker safety

  • Identify potential issues that may affect machine performance

Weather data

  • Meteorological data

  • E.g.: temperature, humidity, precipitation, and wind speed

  • Predict failures due to environmental factors

Weight sensor data

  • weight of materials being processed or transported by the machine


X-ray imaging data

  • Detect internal damage or wear in machinery

  • Using X-ray images






References

Some references are given below to provide reviews of the use of big data analytics and predictive maintenance in industry, as well as the various analytical techniques that can be used to identify patterns and predict equipment failures. Some discussions also include data sources, measurement methods, and benefits of using these approaches.


  1. Huang, Y., & Zhou, J. (2019). A Review of Big Data Analytics for Predictive Maintenance. IEEE Access, 7, 154233-154246.

  2. Valls, M., Karim, R., Azzaro-Pantel, C., & Domenech, S. (2019). Predictive Maintenance in Industry 4.0: A Review. IEEE Transactions on Industrial Informatics, 15(6), 3666-3679.

  3. Sharma, A., Kumar, A., & Singh, R. (2020). Predictive Maintenance: A Comprehensive Review of Techniques and Applications. Journal of Intelligent Manufacturing, 31(6), 1389-1414.

  4. Aksoy, E., Ozkan, M. C., & Bilge, U. (2019). A Survey on Predictive Maintenance: From Classical to Deep Learning Methods. Journal of Intelligent Manufacturing, 30(5), 2025-2049.

  5. Wang, Y., Sun, W., & Duan, H. (2021). A Comprehensive Review on Predictive Maintenance of Industrial Equipment Based on Big Data Analytics. IEEE Transactions on Industrial Informatics, 17(2), 1561-1572.

  6. Martin Pech,Jaroslav Vrchota D andJiří Bednář (2021) Predictive Maintenance and Intelligent Sensors in Smart Factory: Review. Special Issue "Intelligent Sensors in the Industry 4.0 and Smart Factory"

  7. P. Nunes a b, J. Santos a b, E. Rocha (2023) Challenges in predictive maintenance - A review. CIRP Journal of Manufacturing Science and Technology, Volume 40, February 2023, Pages 53-67

By WNPL - 22nd Apr 2023
Custom AI/ML and Operational Efficiency development for large enterprises and small/medium businesses.
Request free consultation
(833) 881-5505

Request free consultation

Free consultation and technical feasibility assessment.
×

Trusted by

Copyright © 2024 WNPL. All rights reserved.