Politecnico di Milano — Publications

Multi-scale split dual calibration network with periodic information for interpretable fault diagnosis of rotating machinery

Article

Conventional intelligent fault diagnosis algorithms based on signal processing and pattern recognition have high demands on expert experience and poor generalization performance, which may not have good fault diagnosis performance in complex industrial fields. Meanwhile, the data acquisition system may suffer from cyber attacks when collecting vibration signals. The vibration signal has a very low signal-to-noise ratio (SNR), which seriously affects the accuracy of fault diagnosis. Aiming at the problem of fault diagnosis under low SNR, a new fault diagnosis framework based on a Multi-scale Split Dual Calibration Network with Periodic Information (PI-MSDCN) is proposed in this paper. In the fault diagnosis framework, a periodic block is constructed to automatically learn the periodic information of vibration signals through the neural network. The learned periodic information and raw vibration signal are used as the input data of MSDCN. Specifically, MSDCN uses convolution kernels of different sizes for different channels of input features to generate multi-scale features, and obtain mixed domain attention features for features with different scales respectively. Then, the attention feature is used as the threshold to remove the redundant information in the multi-scale feature adaptively. Finally, in order to calibrate the contribution of different scale features to fault diagnosis, the mixed-domain attention coefficients are applied to the corresponding features to obtain richer multi-scale attention features. The experimental studies under different levels of interference are performed to demonstrate the average accuracy of the proposed method is 92.91% ( ± 5 . 08 % ), which is superior to other existing results in literature.

Yongyi Chen, Dan Zhang, Hongjie Ni et al. 2023

Publications

MPC-based tracking control for air-breathing hypersonic vehicles with input constraints

Optimal Vibration Control of Vehicle Engine-Body System using Haar Functions

An LMI approach to vibration control of base-isolated building structures with delayed measurements

Neural network-based models for a vibration suppression system equipped with MR brake

The impact of interference cancellation on the uplink throughput of WLAN with CSMA/CA

A dual‐driven event‐triggered mechanism for observer‐based sliding mode control of stochastic uncertain Markov jump systems

Dynamic path planning of mobile robot based on improved simulated annealing algorithm

Comments on “Finite-Time $H_{\infty }$ Fuzzy Control of Nonlinear Jump Systems With Time Delays Via Dynamic Observer-Based State Feedback”

Performance Optimization of Successive Cancellation Detectors

A Linear Parameter-Varying Approach to H∞ Control of an Offshore Wind Turbine

Robust l2-gain control for 2D nonlinear stochastic systems with time-varying delays and actuator saturation

A Robust Observer-Based Sensor Fault-Tolerant Control for PMSM in Electric Vehicles

A convex optimization approach for vibration control of base isolated structures with limited wireless communication capacity

Fuzzy sliding mode control design for a class of disturbed systems

Introduction

A Low Complexity Iterative Receiver based on Successive Cancellation for MIMO

Stability Analysis and Stabilization of Discrete-Time Switched Nolinear Systems with Mode-Dependent Average Dwell Time Under Nested Actuator Saturation

Multi-scale split dual calibration network with periodic information for interpretable fault diagnosis of rotating machinery

Modeling simulations and instrumentation of high pressure roller crusher for the silicon carbide production

TFF-CNN: Distributed optical fiber sensing intrusion detection framework based on two-dimensional multi-features