Editorial

Common Problems of Data Loggers and How to Fix Them

Data logging generally refers to the systematic recording of data. This systematic recording is crucial to maintaining a smooth and efficient work process while safeguarding the products and workforce involved. 

Data loggers are prominent whenever an industry needs to record data at frequent and regular intervals, especially for quality standards, estimations, or standardization. However, despite being efficient tools, data loggers might carry some common problems. Before we talk about them, let’s understand the basics of a data logger. 

What Are Data Loggers? 

Data loggers are devices that continuously record data. A data logger looks like a box full of wires, batteries, and sensors and consists of a module based on digital processors.

Many types of data loggers vary depending on their connectivity, size, and other factors. Therefore, when choosing data loggers you must also keep your specific purpose in mind.

Common Problems and Solutions around Data Loggers

Several common problems surround the working of a data logger. If you’re someone

constantly dealing with technology to enhance your business, you must know how to solve or deal with the problems associated with it. 

Some of the problems considering data loggers are: 

Battery Power Drainage 

Standard data loggers run out of batteries in a short period. With numerous data loggers at a site running simultaneously, it would be a hectic job to change their batteries when they run out. Changing them might take a while, resulting in data gaps and potential danger to the components of the particular environment. Moreover, data loggers in spaces like transportation or areas difficult for constant manual intervention are likely to be disturbed if the battery runs out or there is a faulty power backup.

This issue is one of the easiest ones to tackle. All you need is to navigate through the multiple advanced variations of data loggers. Many data loggers come with automated power backup systems. They create sufficient battery backing as the data logger enters the battery draining phase. Moreover, many data loggers come with alert functions to alarm the concerned authorities in cases of battery drainage. 

Data Storage Limitations 

Data loggers are known for their data accumulation feature. But this feature comes with its limitation. There is no way to predict or track the breakthrough point in setups like research. Therefore, the data logging process might go on for quite a while. This means there will be many data points from various environments and research models.

Data loggers come with limited memory capacity, and once this reaches the exhaustion phase there might be a need to come up with alternate, makeshift methods for data storage. Manual data processing, monitoring, and documentation can be exhausting and prone to mistakes.  

Instead of only relying on one data logger, you can opt for a full-fledged data acquisition system. This system utilizes the data logger technology by adding external support for data storage. It also helps with real-time data acquisition and analysis, further alarming the authorities in case of parameter breaches or other issues hindering adequate environmental conditions. Data acquisition systems come with more memory capacity and help expand the volume of data at hand. 

Network Outages

Many data loggers have a feature to alarm the user if something goes wrong. Whenever the environmental parameters cross the safety ranges, the alarm goes off. However, this feature depends on the network connection. The warning will be missed if the connectivity is lost.

This will prevent the timely response of the employees and invite damages to the concerned elements inside the environment.

Moreover, if the alarms go off as soon as the connectivity restores, there can be network congestion with multiple alarms trying to send delayed alerts simultaneously. It can crash the entire system and lead to data loss.

When there are network outages, you could temporarily switch to a network-independent source to log data as long as the network keeps fluctuating. You can also opt for data acquisition systems to tackle network failures as they can deliver alarms in case of connectivity issues. 

Gaps between the Data Readings

Data loggers are set to record or read specific parameters at a given time interval. For example, if the data logger of a refrigerator is programmed to record the temperature every thirty minutes, there will be a measurement gap between two readings. Any breach occurring during the interval can go unnoticed or spoil the potency of the components stored in the environment. Even if the next reading after the gap confirms a parameter breach, sensitive items like biochemicals, vaccines, organs, blood, or tissues might have already experienced exposure that challenges their efficacy. This gap is a significant issue and can cause serious trouble to the security and sanctity of the products that need to be monitored efficiently. 

To solve this problem, you can set the recording interval to a shorter one. Depending upon the sensitivity of goods, the interval can be of a few seconds or minutes. Data loggers like the WiFi data logger can further help in simultaneous monitoring, documentation, and assessment of the readings, eventually maintaining the quality of the goods. Strategizing the data logger setup according to the vulnerability of the products that need to be monitored is extremely important. 

Apart from the ones mentioned above, there are some more common problems that users have reported with using data loggers. However, none are beyond solutions. 

Companies in every industry today, irrespective of size and structure, are all about upgrading their business technology. The data logger technology is constantly evolving, and there is more space for technological advancement. 

Data loggers have become increasingly helpful for industries in raising their production and manufacturing standards by recording and analyzing data in real-time and providing meaningful reports. Datalogging through a device is significantly more beneficial than manual logging and reduces cost and inaccuracy. 

Simple problems, if any, are also easily fixable, and data loggers are reliable in the long run, especially for industries and enterprises like big pharma, automobile, and food.

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