From reactive maintenance to predictive maintenance: A transformation strategy for fault prediction in thermal power plants

With the development of the times, people's electricity demand has gradually increased. In the current power development, thermal power plants have become an important source of power for urban life and industrial production in China, and the operating status of thermal power plant equipment determines its power supply efficiency and quality. Current thermal power plants are undergoing a transformation from "traditional-digital-intelligent."

Traditional Operation and Maintenance and Inspection Scenarios

The traditional thermal power plant inspection model uses a lot of electrical inspection equipment and has a high frequency, resulting in high labor costs. The inspection areas have safety risks such as high temperature, high altitude, high humidity, and high dust. Equipment such as coal mills, steam turbines, air compressors, and oxidation fans operate with high noise levels, which can damage hearing over time. The coal field and coal conveying belts produce a large amount of dust, and long-term inhalation by inspectors can cause pneumoconiosis. Inspections in some locations such as pits, high altitudes, and confined spaces also pose safety risks.

With equipment aging and accelerated technological iteration, its shortcomings are becoming increasingly prominent: regular maintenance, while diligent, cannot escape the limitations of a "one-size-fits-all" approach, often leading to over-maintenance or insufficient maintenance; after a fault occurs, the fault cannot be located, and the unit must be disassembled one by one after shutdown, increasing maintenance costs, and unplanned shutdowns will also lead to economic losses and power outages. Even worse, due to the lack of accurate data support, the root cause of the fault is difficult to investigate, repeated faults occur frequently, and maintenance costs remain high.

Reshaping Equipment Maintenance with Intelligence

Predictive maintenance solutions are like a ray of dawn, illuminating a new path for thermal power plant equipment maintenance. Through the Internet of Things technology, a large number of sensors are deployed on key equipment such as boilers, electrical heating, steam turbine rotating shifts, furnace control, and machine control, monitoring the equipment's "heartbeat" and "body temperature" 24/7.

Equipment Measurement Point List of a Certain Thermal Power Plant

Through the operation and maintenance management platform, the thermal power plant has achieved refined management of predictive maintenance of equipment failures. Big data analysis and artificial intelligence algorithms perform in-depth mining and analysis of the collected data to identify abnormal signs in equipment operation in advance and predict the occurrence time and location of potential failures. Maintenance personnel can quickly locate equipment abnormalities, assess fault risks, and develop targeted maintenance plans based on visualized data.

△Intelligent Diagnosis and Maintenance Plan of a Certain Thermal Power Plant

At the same time, the platform can intelligently analyze the health status of the equipment, predict the types and impact range of future possible failures, and provide strong support for the operation and maintenance decisions of the thermal power plant.

Helping Thermal Power Plants Accelerate Intelligent Construction

On key thermal power plant equipment such as boilers, steam turbines, and generators, the monitoring effect of predictive maintenance solutions is particularly significant. It can not only monitor key parameters such as vibration, temperature, sound, and pressure of the equipment in real time, but also predict the degree of equipment wear and performance degradation trend through algorithm models, providing maintenance personnel with accurate maintenance suggestions. This "proactive" maintenance method effectively avoids unplanned shutdowns caused by sudden equipment failures and ensures the continuity and stability of power supply.

01 Vertical Refinement: Achieving vertical data refinement from equipment → data → information → knowledge → decision-making;

02 Horizontal Integration: Automatic connection between internal enterprise data and business, without independent business systems;

03 Achieving efficient and reasonable equipment management: Achieving efficient and reasonable equipment management. Predictive maintenance can accurately determine the location of equipment failures and predict the service life, thereby guiding targeted optimization of on-site inventory spare parts.

Reduces or eliminates equipment accidents and personnel injuries, ensuring that workers can safely carry out production and construction. Prevents occupational poisoning and occupational diseases, protects the health of workers, and achieves common development between enterprises and employees.

The Zhongyun Kangchong equipment predictive maintenance solution has promoted the transformation of thermal power plants towards intelligence and green development, contributing to the goal of carbon neutrality!