How to use transformer online monitoring data? The whole process from collection to operation and maintenance decision-making

• Data collection is just the beginning: Online monitoring generates vast amounts of data; without analysis and utilization, this data remains nothing more than a collection of numbers in a database. The process from data to decision-making involves: collection → cleaning → analysis → diagnosis → recommendations → implementation
• Tiered Processing Strategy: Not all data is equally important. Normal data is automatically archived, abnormal data automatically triggers alerts, and severe anomalies directly trigger an emergency response.
• Closed-loop decision-making: The ultimate goal of monitoring is to guide operations and maintenance activities—using data to determine whether maintenance is needed, what needs to be repaired, and when to perform the repairs. Any monitoring that does not form a closed-loop decision-making process is ineffective.

1. Data Processing Workflow

1.1 Data Cleaning—Raw data may contain sensor noise, communication errors, and environmental transient interference. The system first filters out this obvious "dirty" data to ensure that subsequent analysis is based on reliable data.

1.2 Trend Analysis—A single data anomaly may be a fluke; it is a sustained shift in trend that constitutes a true signal. The system automatically tracks the trend curves of key parameters and triggers an alert when a trend accelerates abnormally or changes direction.

1.3 Multidimensional Diagnosis—Conduct correlation analysis on data from different monitoring dimensions. An anomaly in a single dimension is insufficient to make a definitive diagnosis; a high-confidence diagnostic conclusion is reached only when data from multiple dimensions point in the same direction.

1.4 Policy Recommendations—Translating diagnostic findings into specific operational and maintenance recommendations: tiered recommendations such as continuing monitoring, shortening monitoring intervals, scheduling offline re-inspections, or suspending operations for repairs as soon as possible.

2. Frequently Asked Questions FAQ

2.1 Q: What should I do if there’s too much data to go through?

Answer: Once the alert thresholds and automatic diagnostic features have been configured, there is no need to manually review each data point. The system automatically filters out anomalies and sends alerts; operations personnel need only monitor the alert messages and diagnostic recommendations. A comprehensive review and analysis of the data should be conducted periodically (e.g., monthly).

2.2 Q: How do I set appropriate alert thresholds?

Answer: You should not simply use the manufacturer’s default values. Establish a baseline based on the device’s historical operational data, and set the alarm threshold to 1.5 to 2 times the baseline average. If the threshold is too high, false negatives may occur; if it is too low, false positives may occur frequently.

2.3 Q: How long should data be retained?

A: We recommend no less than one full maintenance cycle (typically 1–3 years). Historical data serves as the foundation for trend analysis and equipment condition assessment. Systems with remote backup capabilities provide better data security.

2.4 Q: How can I tell if an alert is genuine or a false alarm?

A: Look at trends rather than isolated data points—a single outlier may be a transient disturbance, while sustained exceedances across multiple cycles indicate a genuine anomaly. Cross-verify: When oil chromatography shows anomalies, check the temperature; when temperature is abnormal, check the load. The reliability of the findings is high when signals from multiple dimensions are consistent.

Disclaimer: The content of this article is for technical exchanges and reference only, and does not constitute any form of procurement commitment or contract offer. Product technical parameters, configuration programs and prices are subject to the actual signed contracts and technical agreements.

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