•The paper proposes a model for Fault Detection and Diagnosis of rotating machinery and validates it on different datasets.•The paper proposes a multi-domain feature set composed of FFT, CWT and raw sensory signals revealing the fault signatures.•The combination of the proposed CLSTM and the multi-domain features outperforms the state-of-the-art FDD models.•A sensitivity analysis is conducted on the burst-length, illustrating its importance on the performance of the FDD models. 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.