Department of CSE, University Centre for Research & Development, Chandigarh University, Mohali, Punjab 140413, India.
Computer Science Department, Jamia Hamdard, Hamdard University, Delhi, India.
Comput Intell Neurosci. 2022 Jun 29;2022:2455259. doi: 10.1155/2022/2455259. eCollection 2022.
Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-world industrial scenarios because of the complex and dynamic relationship between devices. The scope of this research is that it can detect equipment defects and deploys them in a natural production environment. The proposed research is describing an online detection method for system failures based on long short-term memory neural networks. In recent years, deep learning technology has taken over as the primary method for detecting faults. A neural network with a long short-term memory is used to develop an online defect detection model. Feature extraction from sensor data is done using the curve alignment method. Based on long-term memory neural networks, the fault detection model is built (LSTM). In the end, sliding window technology is used to identify and fix the problem: the model's online detection and update. The method's efficacy is demonstrated by experiments based on real data from power plant sensors.
工业 4.0 时代，物联网的广泛应用为通过问题检测提高工业设备的可靠性提供了重大契机。由于设备之间复杂且动态的关系，很难利用统一的模型来描述实际工业场景中设备的工作状态。本研究的范围是能够检测设备缺陷并将其部署在自然生产环境中。本研究提出了一种基于长短时记忆神经网络的系统故障在线检测方法。近年来，深度学习技术已成为故障检测的主要方法。使用具有长短期记忆的神经网络来开发在线缺陷检测模型。使用曲线对齐方法从传感器数据中进行特征提取。基于长短期记忆神经网络，构建故障检测模型（LSTM）。最后，使用滑动窗口技术识别和解决问题：模型的在线检测和更新。该方法通过基于电厂传感器的实际数据的实验得到了验证。
