目前矿井通风系统阻变型故障诊断方法需要收集故障样本方可进行故障位置和故障量诊断，且故障位置诊断和故障量诊断需要分别建立对应分类和回归数学模型。针对矿井通风系统阻变型故障样本收集难度大和故障位置及故障量无法同时进行故障诊断的问题，将矿井通风系统阻变型故障诊断转换为最小欧氏距离的优化求解问题，提出一种无需样本参与训练的矿井通风系统阻变型故障诊断无监督学习模型，利用协方差矩阵自适应进化策略方法对无监督学习模型进行优化求解，实现分类与回归预测一体化。通过进行风量、风压单一特征和风量-风压复合特征的对比模拟试验，结果表明:所提出的故障诊断无监督学习模型和所使用的求解方法可有效地解决矿井通风系统阻变型无样本参与的故障诊断问题;故障诊断过程中无需单独分别进行故障位置和故障量诊断;风量-风压复合特征比风量或风压单一特征下的矿井通风系统阻变型故障诊断可达到更高的故障位置诊断准确率和更低误差的故障量诊断性能;即使选用部分观测点，也可实现较高的故障位置诊断准确率和较低故障量诊断误差的性能，且故障观测点比例大小与诊断性能无直接影响关系。

The present resistance variant fault diagnosis method for mine ventilation system needs to collect fault samples before the fault location and fault volume diagnosis. Furthermore,the mathematical model for classification and regression should be built for the location and volume of the fault diagnosis separately. In terms of the issues of the resistance variant fault diagnosis method,such as,difficulties in collecting the fault samples,and diagnosing the fault location and volume at the same time,the resistance variant fault diagnosis of mine ventilation system was transformed into an optimal problem of minimum Euclidean distance,thus an unsupervised learning model for resistance variant fault diagnosis of mine ventilation system without the training sample was proposed,and the Covariance Matrix Adaptation Evolution Strategy method was used to optimize the unsupervised learning model to realize the integration of classification and regression prediction. A comparable simulation test of air volume,wind pressure single feature and air volume-wind pressure composite characteristics was conducted and the results show that the proposed fault diagnosis model and the solution method can effectively solve the fault diagnosis problem of mine ventilation system resistive type without sample participation. In the process of fault diagnosis,it is not necessary to diagnose fault location and the fault magnitude separately. Compared with the resistance variant fault diagnosis of mine ventilation system under the single feature of air volume or wind pressure,the composite features of air volume-wind pressure can achieve a higher accuracy of fault location diagnosis and a lower error of fault volume diagnosis. Even if some observation points are selected,the model can achieve a higher fault location diagnostic accuracy and a lower fault diagnostic error,and the proportion of fault observation points has no direct influence on the diagnostic effect.