What is Data Logging?
Data logging is the methodical collection of specific information and its storage over a period of time.
It involves the tracking, monitoring, and recording of a variety of events in a system in a measurable manner.
The operations in data logging are achieved using tools called data loggers. It is an electronic device that transfers the collected information to a central location.
Key Components of a Data Logger
A data logger has four key components:
1. Sensors
These are the components that measure and record physical data, such as temperature, movement, or humidity.
2. Memory
This component stores the data that the sensors collect. The capacity of memory determines how frequently the data loggers would need to transfer data.
3. Microprocessor
This component processes the physical data into analyzable formats so that computers are able to generate tables and reports to aid decision-making.
4. Power Source
Data loggers can either be powered via batteries or plugged into a main power source, depending on power needs, situation, and the location of the device.
Types of Data Loggers
There are four key types of data loggers, each one useful in different situations that have specific requirements (like portability or uninterrupted power.)
1. Standalone Data Loggers
Standalone data loggers function independently of a central system and have their own power source and storage.
They are useful for deployment in situations where conditions are hazardous or the power supply is variable.
These data loggers are most commonly used in food storage and transportation to track humidity levels.
2. Wireless Data Loggers
Wireless data loggers leverage networks like Bluetooth or Wi-Fi to collect and transmit data without needing any cables.
Most modern wireless data loggers are cloud-compatible and transmit data directly to the organization’s designated cloud.
The data transmission is significantly faster with these devices, enabling real-time data visibility.
3. Computer-Based Data Loggers
Computer-based data loggers are highly accurate because they leverage software installed on computers with quick processors to collect and analyze data from external sensors.
These setups are commonly found in laboratory settings where accuracy and precision are important.
These data loggers allow users some level of control over data collection, for example, sample sizing.
4. Web-Based Data Loggers
Web-based data loggers are remote devices that record and transmit data through an internet connection.
The data is stored on remote servers, which helps with real-time monitoring for longer periods of time without taxing the device battery too much.
They can be accessed from any device that can connect to and interact with the internet.
Benefits of Electronic Data Logging
Data logging doesn’t just enable real-time collection and storage of event data but also offers several key advantages:
- It helps control operations by monitoring and analyzing real-time data to identify problems.
- Data logging devices reduce the workload on manpower by transmitting local data remotely and reducing personnel visits to a site.
- Electronic data loggers can be deployed in hazardous or risky conditions to prevent danger to manpower.
- Versatile monitoring enables data loggers to be programmed according to the data needs at the processing point.
Applications of Data Logging
Data logging is an activity that can be observed occurring in daily life, such as recording someone’s body temperature at various times of day or checking the daily weather forecast before stepping out of the house.
Some other crucial applications of data logging are:
Machine Diagnostics
In industrial settings where bad machinery can cause significant loss and damage, data logging can help with proactive machine maintenance.
Data loggers transmit machine status data to a centralized console from where its health can be monitored effectively.
Regulatory Compliance
For organizations seeking to improve regulatory compliance, data logging devices and functions can be leveraged for review, analysis, data validation, identification of inconsistencies or anomalies in company data, etc.