Suppressing friction-induced stick-slip vibration through a linear PZT-based absorber and energy harvester

In this paper,a PZT(lead zirconate titanate)-based absorber and energy harvester(PAEH)is used for passive control of friction-induced stick-slip vibration in a friction system.Its stability condition coupled with PAEH is analytically derived,whose efficiency is then demonstrated by numerical simulation.The results show that the structural parameters of the PAEH can significantly affect the system stability,which increases with the mass ratio between the PAEH and the primary system,but first increases and then decreases with the natural frequency ratio between the PAEH and the primary system.The impacts of the electric parameters of the PAEH on the system stability are found to be insignificant.In addition,the PAEH can effectively suppress the stick-slip limit cycle magnitude in a wide working parameter range;however,it does not function well for friction systems in all the working conditions.The stick-slip vibration amplitude can be increased in the case of a large loading(normal)force.Finally,an experiment on a tribo-dynamometer validates the findings of the theoretical study,in which the vibration reduction and energy harvesting performance of the PAEH is fully demonstrated.

Remaining useful life prediction for train bearing based on an ILSTM network with adaptive hyperparameter optimization

Remaining useful life(RUL)prediction for bearing is a significant part of the maintenance of urban rail transit trains.Bearing RUL is closely linked to the reliability and safety of train running,but the current prediction accuracy makes it difficult to meet the re-quirements of high reliability operation.Aiming at the problem,a prediction model based on an improved long short-term memory(ILSTM)network is proposed.Firstly,the variational mode decomposition is used to process the signal,the intrinsic mode function with stronger representation ability is determined according to energy entropy and the degradation feature data is constructed com-bined with the time domain characteristics.Then,to improve learning ability,a rectified linear unit(ReLU)is applied to activate a fully connected layer lying after the long short-term memory(LSTM)network,and the hidden state outputs of the layer are weighted by attention mechanism.The Harris Hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of the LSTM.Finally,the ILSTM is applied to predict bearing RUL.Through experimental cases,the better perfor-mance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated,and its superiority of hyperparameters setting is demonstrated.

Simultaneous energy harvesting and tribological property improvement

In this study,piezoelectric elements were added to a reciprocating friction test bench to harvest friction‐induced vibration energy.Parameters such as vibration acceleration,noise,and voltage signals of the system were measured and analyzed.The results show that the piezoelectric elements can not only collect vibration energy but also suppress friction‐induced vibration noise(FIVN).Additionally,the wear of the friction interface was examined via optical microscopy(OM),scanning electron microscopy(SEM),and white‐light interferometry(WLI).The results show that the surface wear state improved because of the reduction of FIVN.In order to analyze the experimental results in detail and explain them reasonably,the experimental phenomena were simulated numerically.Moreover,a simplified two‐degree‐of‐freedom numerical model including the original system and the piezoelectric system was established to qualitatively describe the effects,dynamics,and tribological behaviors of the added piezoelectric elements to the original system.