BP-Neural-Network-Based Tool Wear Monitoring by Using Wav elet Decomposition of the Power Spectrum

In a drilling process, the power spectrum of the drilling force is related tothe tool wear and is widely applied in the monitoring of tool wear. But the feature extraction andidentification of the power spectrum have always been an unresolved difficult problem. This papersolves it through decomposition of the power spectrum in multilayers using wavelet transform andextraction of the low frequency decomposition coefficient as the envelope information of the powerspectrum. Intelligent identification of the tool wear status is achieved in the drilling processthrough fusing the wavelet decomposition coefficient of the power spectrum by using a BP (BackPropagation) neural network. The experimental results show that the features of the power spectrumcan be extracted efficiently through this method, and the trained neural networks show highidentification precision and the ability of extension.

Tool Wear Monitoring in Drilling Using Multiple Feature Fusion of the Cutting Force

This paper presents a tool wear monitoring method in drilling process using cutting force signal. The kurtosis coefficient and the energy of a special frequency band of cutting force signals were taken as the signal features of tool wear as well as the mean value and the standard deviation from the time and frequency domain. The relationships between the signal feature and tool wear were discussed; then the vectors constituted of the signal features were input to the artificial neural network for fusion in order to realize intelligent identification of tool wear. The experimental results show that the artificial neural network can realize fusion of multiple features effectively, but the identification precision and the extending ability are not ideal owing to the relationship between the features and the tool wear being fuzzy and not certain.

Wear monitoring method of water-lubricated polymer thrust bearing based on ultrasonic reflection coefficient amplitude spectrum

The water-lubricated thrust bearings of the marine rim-driven thruster(RDT)are usually composed of polymer composites,which are prone to serious wear under harsh working conditions.Ultrasonic is an excellent non-destructive monitoring technology,but polymer materials are characterized by viscoelasticity,heterogeneity,and large acoustic attenuation,making it challenging to extract ultrasonic echo signals.Therefore,this paper proposes a wear monitoring method based on the amplitude spectrum of the ultrasonic reflection coefficient.The effects of bearing parameters,objective function,and algorithm parameters on the identification results are simulated and analyzed.Taking the correlation coefficient and root mean square error as the matching parameters,the thickness,sound velocity,density,and attenuation factor of the bearing are inversed simultaneously by utilizing the differential evolution algorithm(DEA),and the wear measurement system is constructed.In order to verify the identification accuracy of this method,an accelerated wear test under heavy load was executed on a multi-functional vertical water lubrication test rig with poly-ether-etherketone(PEEK)fixed pad and stainless-steel thrust collar as the object.The thickness of pad was measured using the high-precision spiral micrometer and ultrasonic testing system,respectively.Ultimately,the results demonstrate that the thickness identification error of this method is approximately 1%,and in-situ monitoring ability will be realized in the future,which is of great significance to the life prediction of bearings.

A Modification of SOM Network(SLFM) and Its Applications in Tool Wear Monitoring and Quality Control

In this paper, a Supervised Linear Feature Mapping(SLFM) algorithm, as a modification of the Kohonen Self Organizing Mapping (SOM),is proposed. The applications in cutting tool wear estimation and quality control and the comparison with a back propagation (BP) algorithm are discussed. The results show that the SLFM algorithm requires less training time and has higher accuracy compared with the BP algorithm. It might be a great potential approach to integrate multi sensor information in process control.

A Review of the Design and Feasibility of Intelligent Water-Lubrication Bearings

Water-lubrication bearings are critical components in ship operation.However,studies on their maintenance and failure detection are highly limited.The use of sensors to continually monitor the working operation of bearings is a potential approach to solve this problem,which is collectively called intelligent bearings.In this literature review,the recent progress of electrical resistance strain gauges,Fiber Bragg grating,triboelectric nanogenerators,piezoelectric nanogenerators,and thermoelectric sensors for in-situ monitoring is summarized.Future research and design concepts on intelligent water-lubrication bearings are also comprehensively discussed.The findings show that the accident risks,lubrication condition,and remaining life of water-lubricated bearings can be evaluated with the surface temperature,coefficient of friction,and wear volume monitoring.The research work on intelligent water-lubricated bearings is committed to promoting the development of green,electrified,and intelligent technologies for ship propulsion systems,which have important theoretical significance and application value.

Wear Debris Analysis: Fundamental Principle of Wear-Graphy

A new wear-graphy technology was developed, which can simultaneously identify the shape and composition of wear debris, for both metals and non-metals. The fundamental principles of the wear-graphy system and its wear-gram system are discussed here. A method was developed to distribute wear debris on a slide uniformly to reduce overlapping of wear debris while smearing. The composition identification ana-lyzes the wear debris using the scanning electron microscope (SEM) energy spectrum, infrared-thermal im-aging and X-ray imaging technology. A wear debris analysis system based on database techniques is demon-strated, and a visible digitized wear-gram is acquired based on the information of wear debris with image collection and processing of the wear debris. The method gives the morphological characteristics of the wear debris, material composition identification of the wear debris, intelligent recognition of the wear debris, and storage and management of wear debris information.

Longs short-term based semi-supervised memory encoder-decoder for early prediction of failures in self-lubricating bearings

The existing knowledge regarding the interfacial forces,lubrication,and wear of bearings in real-world operation has significantly improved their designs over time,allowing for prolonged service life.As a result,self-lubricating bearings have become a viable alternative to traditional bearing designs in industrial machines.However,wear mechanisms are still inevitable and occur progressively in self-lubricating bearings,as characterized by the loss of the lubrication film and seizure.Therefore,monitoring the stages of the wear states in these components will help to impart the necessary countermeasures to reduce the machine maintenance downtime.This article proposes a methodology for using a long short-term memory(LSTM)-based encoder-decoder architecture on interfacial force signatures to detect abnormal regimes,aiming to provide early predictions of failure in self-lubricating sliding contacts even before they occur.Reciprocating sliding experiments were performed using a self-lubricating bronze bushing and steel shaft journal in a custom-built transversally oscillating tribometer setup.The force signatures corresponding to each cycle of the reciprocating sliding motion in the normal regime were used as inputs to train the encoder-decoder architecture,so as to reconstruct any new signal of the normal regime with the minimum error.With this semi-supervised training exercise,the force signatures corresponding to the abnormal regime could be differentiated from the normal regime,as their reconstruction errors would be very high.During the validation procedure for the proposed LSTM-based encoder-decoder model,the model predicted the force signals corresponding to the normal and abnormal regimes with an accuracy of 97%.In addition,a visualization of the reconstruction error across the entire force signature showed noticeable patterns in the reconstruction error when temporally decoded before the actual critical failure point,making it possible to be used for early predictions of failure.