As industries continue to embrace digital transformation, predictive maintenance (PdM) is becoming an integral method for improving the efficiency and reliability of operations. Predictive maintenance leverages data and analytics to schedule maintenance tasks at the precise time when they are most beneficial and cost-effective. Its main goals are to extend the lifespan of company assets, minimize unplanned downtime, and reduce the costs associated with maintaining equipment.
Introduction
Predictive maintenance must ensure maintenance tasks are performed neither too early as to be unnecessary or too late as to cause potential equipment damage or plant shutdowns.
In a typical setup, condition monitoring tools are installed on machinery to continuously collect data on parameters such as vibration, temperature, pressure, and sound. Machine learning and other algorithms are then used on the collected data to identify trends that precede failures and to predict the remaining useful life of the equipment. For example, vibration analysis can detect anomalies in rotating equipment like turbines and motors, indicating potential issues before they lead to failure.
From these predictions, an optimal maintenance schedule can be determined that minimizes maintenance costs while preventing equipment failures. But for this to be possible, a sophisticated data architecture must be in place to collect, store, and analyze data generated by plant equipment.
Implementation Overview
A typical implementation of predictive maintenance is described as follows:
- Data collection: Install sensors and data collection systems on critical equipment to record relevant data.
- Data centralization: Consolidate the collected data into a centralized system where it can be processed and analyzed.
- Analysis and modeling: Apply statistical models and machine learning algorithms to analyze historical and real-time data to predict potential failures.
- Maintenance scheduling: Schedule maintenance tasks based on the results of your models and algorithms.
- Feedback and optimization: Monitor the outcome of your predictive maintenance activities to refine and optimize the predictive models and maintenance schedules.
Key Pain Points
While predictive maintenance can provide significant benefits, especially when it comes to reducing downtime and TCO, it can be difficult for traditional industries to put into practice. Some of the major difficulties that industries encounter are described as follows:
- High initial investment: The initial setup for sensors, data storage, and analytics software can be costly.
- Difficulty of data management: A robust data infrastructure is required to collect, store, and ensure the integrity of the large volumes of diverse data used by predictive maintenance solutions.
- Complexity of data analytics: Data analyst teams must be well-versed in machine learning and modern analytics to understand and get good results from the sophisticated models and algorithms required for predictive maintenance.
How TDengine Can Help
Good predictive maintenance algorithms rely on large, high-quality datasets, and they improve with access to more data. TDengine is designed to make large-scale industrial data more accessible and includes a number of capabilities that enable predictive maintenance use cases:
- Centralization of data across platforms and sites: With TDengine, you can store and process data from different historians and protocols in a unified platform, with built-in ETL to retain context and implement good governance. Your predictive maintenance solution then has a wealth of high-quality data to work with and can deliver more accurate results.
- Open ecosystem with standard interfaces: TDengine can integrate with essentially any third-party predictive maintenance solution through its open interfaces and standard connectors such as JDBC and ODBC. This openness gives you the freedom to test emerging technologies and new products to determine which is the best fit for your use case.
- Real-time publish-subscribe data sharing: For AI and ML applications like predictive maintenance, traditional view-based data sharing is not sufficient: these applications need access to the latest data in real time. With TDengine’s data subscription capability, predictive maintenance applications can subscribe to topics and have relevant data pushed to them whenever an update occurs, ensuring that they are always working with the most up-to-date datasets.
- Unified querying of historical and real-time data: Unlike many platforms, TDengine has no concept of archived data and does not differentiate between querying data recorded ten years ago and ten seconds ago. This means that your applications and algorithms can easily and quickly make use of the data they need for your predictive maintenance strategy, no matter whether that data extends over the lifetime of the equipment or only includes recent metrics.
It’s not an overstatement to say that the effectiveness of predictive maintenance at an organization depends in large part on the suitability of its data infrastructure. With the capabilities mentioned above, combined with its industry-leading performance and scalability, TDengine can help position your predictive maintenance strategy for success.
As a next generation data historian, however, TDengine does not provide predictive maintenance capabilities of its own. Instead, TDengine delivers an open ecosystem where you can integrate your data with any predictive maintenance solution over open interfaces and standard protocols. In this open architecture, you can choose the best solution for your business and let TDengine focus on collecting, consolidating, and transforming your data to ensure that your chosen predictive maintenance product gets the best results possible.
