Sensors used in BigData for enterprise predictive maintenance

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