In the current era of rapid economic development, digital transformation has become a top priority. The importance of data collection is self-evident, yet it often comes with many "pitfalls." This article will guide you through these pitfalls in data collection and provide effective solutions to help you achieve precise data collection.

When collecting data from legacy equipment, many factories tend to fall into the trap of "false data." Since most legacy equipment lacks standard communication protocols and interfaces, data collection becomes more challenging. Some manufacturers, in pursuit of convenience and speed, may resort to collecting current signals to determine equipment operating status. However, this method has significant errors. Taking CNC machines as an example: if only the current signal of the main switch is collected, once the equipment is powered on, it will be recognized as operating, even though it may actually be in standby mode. To obtain real data, we should collect the current signal of the spindle rotation, because only when the spindle rotates does the CNC machine truly produce output.

In addition to false data, data collection from legacy equipment is also prone to the pitfall of "dead data." Dead data refers to data that lacks timeliness and relevance. If equipment output is simply recorded without being linked to key information such as production orders or production progress, this data loses its practical value. Flowing data holds more value—only by closely integrating data with the production process can the true meaning of data collection be realized.

So, how should we proceed? Before collecting data from legacy equipment, we must first clarify the goals of data collection. Is it for monitoring equipment operating status, improving production efficiency, optimizing production processes, or reducing production costs? Only by defining the goals can we select a targeted data collection solution.

Based on the characteristics of legacy equipment, we should choose appropriate collection methods. For equipment without standard interfaces, consider using wireless sensors, IoT technology, or other means for data collection. At the same time, ensure that the collected data is authentic and accurate to avoid generating false or dead data.

After collecting the data, we should strengthen data correlation and analysis. Link key information such as equipment output, production orders, and production progress to achieve real-time data sharing and collaboration. Furthermore, leverage technologies like big data and artificial intelligence to conduct in-depth analysis of the data, uncovering the value hidden within, and providing strong support for factory operations.

Although data collection from legacy equipment presents certain difficulties and challenges, by mastering the correct methods and techniques, we can efficiently and accurately collect authentic and valuable data. Through clarifying data collection goals, selecting appropriate collection methods, and strengthening data correlation and analysis, we can avoid unnecessary detours and wasted expenses, providing robust support for factory operations. Let us keep an eye on new technologies and methods in the field of data collection and jointly drive the digital transformation of factories!