Page 80 - 《真空与低温》2026年第2期

第 32 卷 第 2 期 真空与低温
2026 年 3 月 Vacuum and Cryogenics 199

基 于 振 动 声 学 特 征 的 红 外 探 测 器 用 斯 特 林 制 冷 机

CBM 故 障 诊 断 方 法 研 究

张 璐，申 蕾，韩蓬磊，杨 帅
（中国电子科技集团第十一所，北京 100020）

摘要：红外探测器用斯特林制冷机运行声音具有特异性，此特异性可以直观表征制冷机的可靠性。声音由物
体振动产生，通过对制冷机运行过程中的微振动输出值，即加速度特征值进行有效提取分析，便于开展制冷机
早期 CBM 故障诊断。论文基于某型制冷机的微振动输出值特征，分析制冷机声音异常的诱发因素，从而确定
微振动加速度值的采集方法，获取了健康状态的声音频谱特征。在本研究方法基础上开发了一套工程化的测试
系统，后期通过大量的数据积累建立与故障模式相关的 CBM 诊断模型，起到对制冷机故障诊断、监测和预警的
作用。
关键词：红外探测器；斯特林制冷机；振动声学；声音频谱；故障诊断
中图分类号：TB651；TB133 文献标志码：A 文章编号：1006-7086（2026）02-0199-06
DOI：10.12446/j.issn.1006-7086.2026.02.011

Research on CBM Fault Diagnosis Method for Infrared Detectors Using Stirling
Refrigerators Based on Vibrational Acoustic Characteristics

ZHANG Lu，SHEN Lei，HAN Penglei，YANG Shuai
（The China Electronic Science and Technology Group No. 11 Institute，Beijing 100020，China）

Abstract： The operating sound of the Stirling refrigerator used in infrared detectors exhibits distinct characteristics，
which can serve as a direct indicator of the equipment's reliability. Since sound is produced by mechanical vibrations，the sub-
tle micro-vibrations generated during the refrigeration machine’s operation contain valuable information about its internal
condition. By precisely measuring and analyzing the acceleration-based micro-vibration output signals，it becomes possible to
identify early signs of potential malfunctions. This study focuses on a particular model of Stirling refrigeration，aiming to in-
vestigate the root causes behind abnormal operational sounds. The data acquisition setup ensures minimal noise interference
and high temporal fidelity， enabling the extraction of reliable characteristic parameters under healthy operating conditions.
From this baseline data， the typical sound spectrum profile of healthy refrigeration unit was identified and documented.
Building upon this foundation， an integrated engineering test system was designed. Over time， continuous data collection
from multiple units under various working conditions allowed for the accumulation of a comprehensive dataset encompass-
ing both normal and faulty states. Leveraging machine learning algorithms and statistical pattern recognition methods，a Con-
dition-Based Maintenance （CBM） diagnostic model was developed. This model correlates specific changes in micro-vibra-
tion signatures with known fault modes. As a result，the proposed methodology enables not only real-time health monitoring
but also predictive maintenance capabilities. By detecting deviations from the established healthy sound spectrum at an early
stage，the system can issue timely warnings before catastrophic failure occurs. Furthermore，the non-invasive nature of vibra-
tion-based monitoring makes it highly suitable for integration into existing infrastructure without requiring major modifica-
tions. The research thus provides a practical and scalable solution for improving the reliability and sustainability of refrigera-
tion machines in precision instrumentation applications.
Key words：infrared detector；Stirling refrigerator；acoustic vibration；sound spectrum；fault diagnosis

收稿日期：2025−12−01
作者简介：张璐，高级工程师。E-mail：luzbit@163.com