Often asset managers ponders if online and real-time condition monitoring is the key to digital transformation. Online condition monitoring is critical for a reliable asset management strategy. However, a comprehensive insight of the emerging trends and challenges in this field is missing. Read along to learn more!

Why should we choose online condition monitoring?

There are several benefits of online condition monitoring viz., early fault detection and better maintenance budgeting based on deeper insights. The whole point of digital sensors is to improve asset availability, reliability and reduce outage risk. It improves proactive decisions on maintenance, management, and load planning on the asset. Reliable asset management strategies aligns with condition monitoring approaches to minimize maintenance expenses and extend machine lifespan.

However, lack of proper training, high cost, and relatively shorter lifespan of monitors are some of the inevitable cons. Often triggering of false alarms and erroneous data interpretation due to sensor malfunctions or temporary fluctuations are also challenging. An interesting use-case is integrating aged assets with compatible monitors to the supervisory control and data acquisition (SCADA) of transformers.

Is online data collection sufficient?

It suffices that only relying on online monitors is not sufficient. Additionally, the real challenge is storage, management and analysis of the large volume of data generate by these monitors. It can be more challenging for organizations that lack necessary infrastructure or data analysis capabilities.

What can be the best practices in data management for online monitoring?

  1. Data collection: Digital sensors generate a vast amount of data regarding on machine’s condition. After identifying the key condition alerting parameters, installation and commissioning of high resolution and frequency hardware devices is the first step towards problem solving.
  2. Data storage and processing: There is a need to store data inside cloud platforms or even in physical data-centers. Though the later would be more expensive and with higher carbon footprints. Without processing, it can be challenging to effectively utilize this data in its raw form. Suitable analytical tools can perform data cleaning to extract meaningful insights and identify relevant trends or anomalies.
  3. Access to Actionable Information: Analytics software plays a crucial role in processing and interpreting the collected data. It helps transform raw data into actionable information by applying algorithms and models that can detect patterns, identify potential faults, and provide insights for decision-making. Without such software, the collected data may remain unutilized or lack the necessary analysis to drive proactive maintenance actions.
  4. Early Fault Detection: An analytics software can detect and diagnose faults by analyzing sensor data and identify early warning signs of developing faults. By overlooking faults, risk of unexpected failures and subsequent damages increases.
  5. Efficient Maintenance Planning: Analytics software enables the development of predictive maintenance strategies based on the condition of the transformer. It assists in determining the optimal timing for maintenance activities and resource allocation. Without this software, maintenance planning may rely on less effective methods, such as reactive or time-based maintenance, resulting in suboptimal utilization of resources and potentially increased maintenance costs.
  6. Trend Analysis: Trend analysis is crucial for understanding the long-term performance of a machine. It helps in identifying patterns and deviations from normal behavior and help in identifying anomalies. Without it, trend analysis becomes difficult, hindering the ability to track and interpret changes in the machine’s condition over time.
  7. Reduced Manual Effort: Without data management, the analysis and interpretation of sensor data may rely heavily on manual efforts. This can be time-consuming, labor-intensive, and prone to human errors. Automated analytics software streamlines the data analysis process, enabling more efficient and accurate interpretation of the sensor data.
  8. Improved Scalability: Effective data management can help in scaling up the condition monitoring system to accommodate a larger number of machines or additional sensors, that can otherwise challenging. The lack of automated data analysis and management tools may limit the scalability of the monitoring infrastructure, making it difficult to handle the increased data volume and complexity

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