Appendix
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.
* Note: The "other benefits" column gives any other benefits than the common benefits of analyzing these data:
predict failure
optimize maintenance
reduce downtime
Name of Data Source |
Measurement |
Other Benefits* |
Accelerometer data |
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Ambient temperature data |
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Asset tracking data |
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Audio data |
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Augmented reality data |
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Barcode data |
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Barometer data |
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Chemical sensor data |
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Computer-aided design (CAD) data |
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Computerized maintenance management system (CMMS) data |
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Conductivity sensor data |
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Current sensor data |
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Customer feedback data |
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Cybersecurity data |
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Drones data |
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Electrical data |
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Electrical usage data |
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Electronic control module (ECM) data |
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Electronic control unit (ECU) data |
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Energy consumption data |
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Energy usage data |
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Environmental data |
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Environmental sensor data |
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Equipment manuals |
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Failure history data |
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Flow meter data |
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Flow sensor data |
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Fluid analysis data |
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Fluid analysis data |
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Fuel consumption data |
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Gas chromatography data |
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Gas sensor data |
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Geographic information system (GIS) data |
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GPS data |
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Hall-effect sensor data |
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Heat map data |
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Humidity data |
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Humidity sensor data |
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Image analysis data |
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Impedance-based sensor data |
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Industrial robots data |
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Infrared imaging data |
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Infrared thermography data |
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Laser displacement sensor data |
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Laser scanning data |
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Level sensor data |
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Lidar data |
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Load cell data |
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Load data |
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Machine log data |
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Machine vision data |
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Magnetic resonance imaging (MRI) data |
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Magnetic sensor data |
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Magnetic stripe reader data |
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Maintenance logs |
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Maintenance records |
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Manufacturing execution system (MES) data |
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Microphone data |
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Mobile device data |
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Motion sensor data |
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Noise level data |
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Performance data |
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PLC data |
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Power quality data |
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Pressure sensor data |
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Process data |
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Radar data |
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Remote monitoring data |
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RFID data |
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SCADA data |
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Social media data |
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Spectroscopy data |
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Spectroscopy sensor data |
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Strain gauge data |
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Tachometer data |
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Telemetry data |
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Temperature data |
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Thermal imaging data |
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Time series data |
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Torque sensor data |
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Ultrasonic data |
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Ultraviolet (UV) sensor data |
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Vibration data |
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Video surveillance data |
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Voltage sensor data |
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Water quality data |
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Wearable sensor data |
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Weather data |
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Weight sensor data |
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X-ray imaging data |
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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.
Huang, Y., & Zhou, J. (2019). A Review of Big Data Analytics for Predictive Maintenance. IEEE Access, 7, 154233-154246.
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.
Sharma, A., Kumar, A., & Singh, R. (2020). Predictive Maintenance: A Comprehensive Review of Techniques and Applications. Journal of Intelligent Manufacturing, 31(6), 1389-1414.
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.
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.
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"
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