Fault Detection and Diagnosis (FDD) of rotating machinery plays a key role in reducing the maintenance costs of the manufacturing systems. How to improve the FDD accuracy is an open and challenging issue. To make full use of signals and reveal all the fault features, this paper proposes a new feature engineering model which combines Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT) and statistical features of raw signals. Then a novel Convolutional Long Short-Term Memory (CLSTM) is developed to understand and classify these multi-channel array inputs. In order to evaluate the effectiveness of the proposed model, three different datasets are used. The paper performs a sensitivity analysis on the input channels to evaluate the efficiency of the proposed multi-domain feature set in different DL architectures, where CLSTM shows its superiority in understanding the feature set. Secondly, a comprehensive review of the state-of-the-art models is conducted, and twelve algorithms are chosen for the comparison to evaluate the performance of the proposed FDD model. The paper also performs an input length sensitivity analysis, showing that the proposed model can achieve 100 % of accuracy with shorter inputs compared to other models, meaning that it causes less delay in an online condition monitoring system. The results demonstrate the superiority of the proposed model over the state-of-the-art models in terms of accuracy on different datasets.

旋转机械的故障检测与诊断（FDD）在降低制造系统的维护成本方面起着关键作用。如何提高FDD的精度是一个开放且具有挑战性的问题。为了充分利用信号并揭示所有故障特征，本文提出了一种新的特征工程模型，该模型结合了快速傅里叶变换（FFT），连续小波变换（CWT）和原始信号的统计特征。然后，开发了一种新颖的卷积长短期存储器（CLSTM）来理解和分类这些多通道阵列输入。为了评估所提出模型的有效性，使用了三个不同的数据集。本文对输入通道进行了敏感性分析，以评估在不同DL体系结构中提出的多域特征集的效率，CLSTM在理解功能集方面显示出优越性。其次，对最新模型进行了全面回顾，并选择了十二种算法进行比较，以评估所提出的FDD模型的性能。该论文还进行了输入长度敏感性分析，表明与其他模型相比，所提出的模型在较短的输入下可以达到100％的精度，这意味着它在在线状态监测系统中的延迟较小。结果表明，在不同数据集的准确性方面，所提出的模型优于最新模型。然后选择十二种算法进行比较，以评估所提出的FDD模型的性能。该论文还进行了输入长度敏感性分析，表明与其他模型相比，所提出的模型在较短的输入下可以达到100％的精度，这意味着它在在线状态监测系统中的延迟较小。结果表明，在不同数据集的准确性方面，所提出的模型优于最新模型。然后选择十二种算法进行比较，以评估所提出的FDD模型的性能。该论文还进行了输入长度敏感性分析，表明与其他模型相比，所提出的模型在较短的输入下可以达到100％的精度，这意味着它在在线状态监测系统中的延迟较小。结果表明，在不同数据集的准确性方面，所提出的模型优于最新模型。