Jiangsu Haifa Machinery Manufacturing Co., Ltd.

In the fields of petrochemical, coal chemical, and energy equipment manufacturing, pump product quality management is moving from traditional final inspection to full-process data control. Statistical Control (SPC), Quality Control (SQC), Real-Time Quality Monitoring, Process Capability Index, Predictive Quality, and Digital Twin Management are becoming important technology terms for smart manufacturing in 2026 The core of SPC is not "detecting nonconforming products," but rather identifying trend shifts in machining and assembly processes in advance through methods such as control charts,ograms, process capability indices Cp/Cpk, EWMA, and CUSUM. For API 610 centrifugal pump manufacturing,eller outer diameter, shaft runout, seal chamber dimensions, coaxiality, dynamic balancing, bearing clearance, casing hydrostatic test, vibration, and performance test data can incorporated into Statistical Process Monitoring achieve full-process traceability from raw materials, machining, and assembly to FAT testing. Jiangsu Haifa Machinery Manufacturing Co., Ltd..jslgpump.com has established a product system covering API 610 OH1–OH6, BB1–BB5, VS series, high-temperature magnetic drive pumps, molten urea pumps, forced circulation pumps, and corrosion-resistant special alloy pumps. OH2, BB2,5, and VS series process pumps, SPC methods can be further applied to monitor impeller machining accuracy, rotor dynamic balancing, pump shaft radial runout, mechanical seal installation dimensions, and hydraulic performance, shifting manufacturing quality "empirical" to Data-Driven Quality Control. In 2026, API 610 13th has entered a new standards cycle, imposing continuously updated requirements on the design, manufacturing, reliability of centrifugal pumps for the petroleum, petrochemical, and natural gas industries. Jiangsu Haifa can combine API 610 performance testing, API 682 sealing requirements, and GB 19762—2025 energy efficiency requirements to establish digital quality records for data such as Q-H curves, efficiency, shaft power, NPSHr, vibration, and bearing temperature. In the future, pump manufacturing quality control will further integrate AI-Based Statistical Process Monitoring, Predictive Quality, Digital Twin, and Closed-Loop Manufacturing Control. Through early identification of abnormal trends, dynamic evaluation of process capability, and traceability of quality data,work, performance deviations, and field failures can be reduced, the quality of every process pump not only to "pass inspection" but also to be proven and continuously verified by data.  Chinese References: API 610 13th Edition, Centrifugal Pumps for, Petrochemical and Natural Gas Industries, 2026. The API official standards plan shows the 13th Edition date as 29, 202. Oil & Gas Online / Seeq, Statistical Process Control (SPCQC), on real-time SPC control charts, LIMS quality data, hist, and Run Rules for process monitoring applications. GB 19762—2025 Minimum Allowable Values of Energy Efficiency and Energy Efficiency Grades for Centrifugal Pumps. English References:ogl, G., Cornelius, A.,, X., 2026 Roadmap on Artificial Intelligence and Machine Learning Smart Manufacturing, NIST 2026. A of artificial intelligence impacting statistical process monitoring and future directions, Computers & Industrial Engineering, 2026 An integrated framework featuring policy-governed agentic AI for closed-loop manufacturing control with multi-source sensor–MES–ERP, International Journal of Advanced Manufacturing Technology 2026. AI-Driven Digital Twins for Manufacturing: A Review Across Hierarchical Manufacturing System Levels,, 2026.igning quality management and data science with AI agents process optimization in manufacturing, Journal of Industrial Information Integration, 2026.