In the monitoring process of petrochemical equipment rotating machinery, the collected large data easily lead to valuable data loss in the pre-processing process and affecting the accuracy of the fault diagnosis. This paper proposes a method for the fault diagnosis of the rotating machinery based on the wavelet-domain denoising and metric distance. The wavelet-domain denoising uses wavelet coefficients of signal and noise that have different properties on different scales and process noisy signal wavelet coefficients. Metric distance is to compare two independent statistical samples with each other after denoising to determine whether they belong to the same sample. First, the denoising of the vibration timedomain signal is based on the wavelet-domain denoising method. Then, the tested fault samples are compared with the known fault samples by metric distance. Finally, the fault types are identified according to the metric distance. Verification of the algorithm performance and the simulation experiment of petrochemical large units show that the method is not only simple and effective but also has better faults recognition. It can guide the faults diagnosis of large petrochemical units and other large units rotating machinery. INDEX TERMS Fault diagnosis, wavelet domain denoising, metric distance, rotating machinery.

The whole process of the petrochemical industry involves flammable and explosive dangerous goods. The timely discovery of abnormalities or failures in the petrochemical process is crucial to ensure production safety. This paper sets up the approach to build the Digital Twin System (DTs) of a petrochemical process. Specifically, we decompose the petrochemical process into five levels one by one and build a digital twin plug-in for each component of the component layers, and then inversely decouple the process to assemble the DTs layer by layer. As a specific experimental example, the characteristic DTs is proposed to build modules of temperature field and pressure field and flow field, these DT modules are driven by practical industrial sampling data from cracking furnace, and three characteristic DTS modules stated above are integrated to form DTS. Based on the digital twin technology and DTs, we propose the logical structure of chemical process status monitoring and fault diagnosis in detail, which improves the safety and controllability of the petrochemical process.

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The time and frequency domain features of a petrochemical unit have a variety of effects on the fault type of bearings, and the signal exhibits nonlinearity, unpredictability, and ergodicity. The detection system's important data are disrupted by noise, resulting in a huge number of invalid and partial records. To reduce the influence of these factors on feature extraction, this work presents a method for the fault feature extraction of bearings for the petrochemical industry and for diagnosis based on high-value dimensionless features. Effective data are extracted from the obtained data using a complex data preprocessing approach, and the dimensionless index is expressed. Then, based on the distribution rule of the dimensionless index, the high-value dimensionless features are retrieved. Finally, to ensure sample completeness, a high-value dimensionless feature augmented model is developed. This approach is applied to the bearing fault experiment platform of a petrochemical unit to effectively classify the bearing fault features, which benefits theoretical guidance for the feature extraction of bearings for a petrochemical unit.