Dynamic modeling of gearbox faults: A review

The reach of tribology has expanded in diverse fields and tribology related research activities have seen immense growth during the last decade. This review takes stock of the recent advances in research pertaining to different aspects of tribology within the last 2 to 3 years. Different aspects of tribology that have been reviewed including lubrication, wear and surface engineering, biotribology, high temperature tribology, and computational tribology. This review attempts to highlight recent research and also presents future outlook pertaining to these aspects. It may however be noted that there are limitations of this review. One of the most important of these is that tribology being a highly multidisciplinary field, the research results are widely spread across various disciplines and there can be omissions because of this. Secondly, the topics dealt with in the field of tribology include only some of the salient topics (such as lubrication, wear, surface engineering, biotribology, high temperature tribology, and computational tribology) but there are many more aspects of tribology that have not been covered in this review. Despite these limitations it is hoped that such a review will bring the most recent salient research in focus and will be beneficial for the growing community of tribology researchers.

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A review of recent advances in tribology

Review on dynamics of cracked gear systems

As a typical manufacturing equipment, CNC machine tool (CNCMT) is the mother machine of industry. Fault of CNCMT might cause the loss of precision and affect the production if troubleshooting is not timely. Therefore, the reliability of CNCMT has a big significance. Predictive maintenance is an effective method to avoid faults and casualties. Due to less consideration of the status variety and consistency of CNCMT in its life cycle, current methods cannot achieve accurate, timely and intelligent results. To realize reliable predictive maintenance of CNCMT, a hybrid approach driven by Digital Twin (DT) is studied. This approach is DT model-based and DT data-driven hybrid. With the proposed framework, a hybrid predictive maintenance algorithm based on DT model and DT data is researched. At last, a case study on cutting tool life prediction is conducted. The result shows that the proposed method is feasible and more accurate than single approach.

A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin

Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme

An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme

A statistical feature investigation of the spalling propagation assessment for a ball bearing

The effects of spur gear tooth spatial crack propagation on gear mesh stiffness

An integral part of modern manufacturing process management is to acquire useful information from machining processes to monitor machine and tool condition. Various models have been introduced to detect, classify, and predict tool wear, as a key parameter of the machining process. In more recent developments, sensor-based approaches have been attempted to infer the tool wear condition from real-time processing of the measurement data. Experiments show that the physics-based prediction models can include large uncertainties. Likewise, the measurement-based (or sensor-based) inference techniques are affected by sensor noise and measurement model uncertainties. To manage uncertainties and noise of both methods, a hybrid framework is proposed to fuse together the results of the prediction model and the measurement-based inference data in a stepwise manner. The fusion framework is an extension to the regularized particle filtering technique, used to facilitate updating the state prediction with a numerical inference model, when measurement models alone are not satisfactory. The results show significant improvement in tool wear state estimation, reducing the prediction errors by almost half, compared to the prediction model and sensor-based monitoring method used independently.

Wennian Yu

Papers

Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme

An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme

A statistical feature investigation of the spalling propagation assessment for a ball bearing

The effects of spur gear tooth spatial crack propagation on gear mesh stiffness

Hybrid data-driven physics-based model fusion framework for tool wear prediction