Early fault detection in squirrel cage induction motor (SCIM) can minimize the downtime and maximize production. This paper presents an adaptive gradient optimizer based deep convolutional neural network (ADG-dCNN) technique for bearing and rotor faults detection in squirrel cage induction motor. Multiple MEMS accelerometers have been used for vibration data collection, and sensor data fusion is employed in the model training and testing. ADG-dCNN allows the automatic feature extraction from the vibration data and minimizes the need for human expertise and reduces human intervention. It eliminates the error caused by manual feature extraction and selection, which is dependent on prior knowledge of fault types. This paper presents an end-to-end learning fault detection system based on deep CNN. The dataset for training and testing of the proposed method is generated from the test set-up. The proposed classifier attained an average accuracy of 99.70%. This paper also presents the recently developed SHapley Additive exPlanations (SHAP) methodology for evaluation of fault classification from the proposed model. The proposed technique can also be extended to other machinery with multiple sensors owing to its end-to-end learning abilities.

鼠笼式感应电机 (SCIM) 的早期故障检测可以最大限度地减少停机时间并最大限度地提高产量。本文提出了一种基于自适应梯度优化器的深度卷积神经网络 (ADG-dCNN) 技术，用于检测鼠笼式感应电机中的轴承和转子故障。多个MEMS加速度计已用于振动数据采集，并在模型训练和测试中采用传感器数据融合。ADG-dCNN 允许从振动数据中自动提取特征，最大限度地减少对人类专业知识的需求并减少人工干预。它消除了依赖于故障类型的先验知识的手动特征提取和选择带来的错误。本文提出了一种基于深度CNN的端到端学习故障检测系统。用于训练和测试所提出方法的数据集是从测试设置中生成的。所提出的分类器的平均准确率为 99.70%。本文还介绍了最近开发的 SHapley Additive exPlanations (SHAP) 方法，用于从所提出的模型评估故障分类。由于其端到端的学习能力，所提出的技术也可以扩展到具有多个传感器的其他机器。