Early fault detection and fault prognosis are crucial functions to ensure safe process operations. Fault prognosis can detect and isolate early developing faults as well as predict fault propagation. To promptly detect potential faults in process systems, it is important to examine the fault symptoms as early as possible. In recent years, fault prognosis approaches have led to the remaining useful life prediction. Therefore, in a process system, advancing prognosis approaches will be beneficial for early fault detection in terms of process safety, and to predict the remaining useful life, targeting the system's reliability. In data-driven models, early fault detection is regarded as a time-dependent sequence learning problem; the future data sequence is predicted using the previous data pattern. Studying recent years' research shows that a recurrent neural network (RNN) can solve the sequence learning problem. This paper proposes an early potential fault detection approach by examining the fault symptoms in multivariate complex process systems. The proposed model has been developed using the Convolutional Neural Network (CNN)- Long Short-Term Memory (LSTM) approach to forecast the system parameters for future sampling windows' recognition and an unsupervised One-class-SVM used for fault symptoms' detection using forecasted data window. The performance of the proposed method is assessed using Tennessee Eastman process time-series data. The results confirm that the proposed method effectively detects potential fault conditions in multivariate dynamic systems by detecting the fault symptoms early as possible.

早期故障检测和故障预测是确保安全过程操作的关键功能。故障预测可以检测和隔离早期发展的故障以及预测故障传播。为了及时发现过程系统中的潜在故障，尽早检查故障症状很重要。近年来，故障预测方法导致剩余使用寿命预测。因此，在过程系统中，推进预测方法将有利于过程安全方面的早期故障检测，以及预测剩余使用寿命，针对系统的可靠性。在数据驱动模型中，早期故障检测被认为是一个时间相关的序列学习问题；使用先前的数据模式预测未来的数据序列。近年来的学习 研究表明，循环神经网络 (RNN) 可以解决序列学习问题。本文通过检查多元复杂过程系统中的故障症状，提出了一种早期潜在故障检测方法。所提出的模型是使用卷积神经网络 (CNN)-长短期记忆 (LSTM) 方法开发的，用于预测未来采样窗口识别的系统参数，以及用于故障症状检测的无监督 One-class-SVM预测数据窗口。使用田纳西伊士曼过程时间序列数据评估所提出方法的性能。结果证实，所提出的方法通过尽早检测故障征兆，有效地检测出多元动态系统中的潜在故障状况。