[1]景 杰,尚鹏杰,郭永昌,等.火电厂先导式气力输灰智能监测方法与应用[J].机械与电子,2026,44(06):55-62.
　JING Jie,SHANG Pengjie,et al.Intelligent Monitoring Method and Application for a Pilot-Operated Pneumatic Ash Conveying Systems in Thermal Power Plants[J].Machinery & Electronics,2026,44(06):55-62.

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火电厂先导式气力输灰智能监测方法与应用()

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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:

44

期数:

2026年06期

页码:

55-62

栏目:

智能检测

出版日期:

2026-06-27

文章信息/Info

Title:

Intelligent Monitoring Method and Application for a Pilot-Operated Pneumatic Ash Conveying Systems in Thermal Power Plants

文章编号:

1001-2257 ( 2026 ) 06-0055-08

作者:

景 杰 ¹ ; 2 ; 尚鹏杰 ¹ ; 郭永昌 ¹ ; 廉自生 ¹

1. 太原理工大学机械工程学院,山西 太原 030024 ;

?2. 山西漳山发电有限责任公司,山西 长治 046021

Author(s):

JING Jie¹ ;  2 ;  SHANG Pengjie¹ ;  GUO Yongchang¹ ;  LIAN Zisheng¹

( 1.College of Mechnical Engineering , Taiyuan University of Technology , Taiyuan 030024 , China ;

2.Shanxi Zhangshan Power Generation Co. , Ltd. , Changzhi 046021 , China )

关键词:

火电厂; 气力输送; 智能监测;  LoRa ; 运行可靠性

Keywords:

thermal power plant ;  pneumatic conveying ;  intelligent monitoring ;  LoRa protocol ;  operational reliability

分类号:

TM621

文献标志码:

A

摘要:

针对火电厂长距离气力输灰过程中易发的管道堵塞、传统监测手段效率低下以及由此导致的系统运行可靠性差、运维成本高等问题,以先导式气力输灰系统为研究对象,提出了状态监测方法并基于此搭建了一套智能状态监测系统。首先,深入解析长距离管道沿程压力梯度的演化机理,基于输灰系统工作模式提出了有效作业片段提取的自适应时间窗口算法;其次,针对低频稀疏采样压力数据的时空特征,定义了趋势一致性系数与局部趋势背离度特征指标,并构建了分层诊断决策模型,实现了对输送过程中 2 类典型故障的精准识别。最后,基于 LoRa 组网协议与无线压力传感器,研制了集感知、传输、分析于一体的智能状态监测系统。研究结果表明,引入所提系统后,故障发现及定位时间因系统直接推送坐标而缩短 90% 以上,显著提升了故障响应效率,有效降低了人工巡检频次,为长距离气力输送系统的智能化升级提供了全链条技术方案,具有重要的工程应用价值。

Abstract:

Long distance pneumatic ash conveying systems in thermal power plants are susceptible to pipeline blockages , and conventional monitoring approaches are often inefficient , resulting in poor operational reliability and high maintenance costs.Focusing on a pilot operated pneumatic ash conveying system , this study proposes a condition monitoring method and develops a corresponding intelligent condition monitoring system.First , the evolution mechanism of the pressure gradient along the long distance pipeline is thoroughly investigated , and based on the operating mode of the conveying system , an adaptive time window algorithm is designed to extract effective conveying segments.Second , to characterize the spatiotemporal features of the low frequency , sparsely sampled pressure data , two indicators — the trend consistency coefficient and the local trend deviation degree — are defined.A hierarchical diagnostic decision model is then constructed , enabling accurate identification of two typical faults during the conveying process.Final- ly , an intelligent condition monitoring system that integrates sensing , data transmission , and analysis is developed using the LoRa networking protocol and wireless pressure sensors.Results demonstrate that the proposed system reduces the time required for fault detection and localization by over 90% by actively pushing fault location coordinates , thereby significantly enhancing fault response efficiency and effectively reducing manual patrol frequency.This study provides a full-chain technical solution for the intelligent upgrade of long-distance pneumatic conveying systems , possessing substantial engineering application value.

备注/Memo

备注/Memo:

收稿日期: 2026-04-22

基金项目:国家自然科学基金资助项目( 52175058 );山西漳山发电有限责任公司重点科技项目( ZBA272301301 )

作者简介:景 杰 ( 1982- ),男,山西运城人,博士研究生,正高级工程师,研究方向为气力输送无线感知与智能监测;尚鹏杰 ( 2001- ),男,河北邯郸人,硕士研究生,研究方向为气固二相流理论研究与智能监测;郭永昌 ( 1981- ),男,山西太原人,博士,实验师,研究方向为热力发电厂节能环保技术,通信作者, E-mail : gyc37@126.com ;廉自生 ( 1962- ),男,山西运城人,博士,教授,研究方向为综采装备机电一体化技术。

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