Deep transfer residual variational autoencoder with multi-sensors fusion for tool condition monitoring in impeller machining

In-process damage to a cutting tool degrades the surface finish of the job shaped by machining and causes a significant financial loss. This stimulates the need for Tool Condition Monitoring (TCM) to assist detection of failure before it extends to the worse phase. Machine Learning (ML) based TCM has been extensively explored in the last decade. However, most of the research is now directed toward Deep Learning (DL). The “Deep” formulation, hierarchical compositionality, distributed representation and end-to-end learning of Neural Nets need to be explored to create a generalized TCM framework to perform efficiently in a high-noise environment of cross-domain machining. With this motivation, the design of different CNN (Convolutional Neural Network) architectures such as AlexNet, ResNet-50, LeNet-5, and VGG-16 is presented in this paper. Real-time spindle vibrations corresponding to healthy and various faulty configurations of milling cutter were acquired. This data was transformed into the time-frequency domain and further processed by proposed architectures in graphical form, i.e., spectrogram. The model is trained, tested, and validated considering different datasets and showcased promising results. 

Health Monitoring of Milling Tool Inserts Using CNN Architectures Trained by Vibration Spectrograms

Whether in the industrial, medical, or real-world domains, more and more data are being collected. The common particularity of all these application domains is that a great part of this data is mostly unlabeled. Thus, designing a learning model with a minimum of labeled data represents a major challenge in the coming years. A particular emphasis has recently been put on unsupervised learning methods based on the idea of autoencoding. The objective of these methods is twofold: to reduce the dimensionality of the input space and to reconstruct the original observation from this lower dimensional representation space. The variational form of these autoencoders, called the Variational Autoencoders (VAEs), is particularly successful in almost all application areas. This enthusiasm comes from the fact that VAEs allow to take advantage of the theoretical foundations of the Variational Bayesian methods and the learning capabilities of artificial neural networks. This review paper gives to the PHM community a synthesis of the latest publications in the PHM domain using the VAEs related to four topics: 1) Data-Driven Soft Sensors for missing values and data outliers, 2) reconstruction error for fault detection, 3) resampling approach for imbalanced data generation and minority class and 4) the variational embedding as PHM preprocessing pipelines and data transformations. After a review of the theoretical foundations and some practical tricks to succeed the implementation of the VAEs in industrial applications, the four main topics used to exploit the VAEs in the PHM domain are detailed. Finally, a global view of the research done at the research institute of Hydro-Québec regarding the diagnosis and failure detection of hydro-generators with VAEs are presented. 

Recent Research and Applications in Variational Autoencoders for Industrial Prognosis and Health Management: A Survey

The spindle current of a machine tool contains a wealth of machining information and by analyzing its signal characteristics wear monitoring of milling tools can be achieved. Most current research in tool wear monitoring has been achieved by intelligent classification methods, but such methods are unable to identify wear states when cutting parameters change. To address this challenge, the paper proposes a new tool wear monitoring framework that identifies the wear state of milling cutters under different cutting parameters. The first step is to establish the mapping relationship between the spindle current signal and the milling force signal through neural networks under a variety of cutting parameters. Based on the mapping property between two signals, a TCN-LSTM based neural network prediction model is proposed to achieve accurate prediction of milling forces by spindle current signal. Various cutting information is then calculated based on the predicted milling forces and PLC communication, and a method is proposed to identify the machining status of the tool: the milling force coefficient monitoring method. The method reflect the wear status of the milling cutter by monitoring the range of threshold values, making the identification of the tool wear state without interference from variable spindle speeds, feeds and depths of cut. The proposed method provides a new solution for tool wear monitoring, breaking the inherent thinking of intelligent tool wear classification, and the experimental results prove the effectiveness and generality of the proposed method. 

Intelligent monitoring of milling tool wear based on milling force coefficients by prediction of instantaneous milling forces

Hypertension is a serious global public health issue. Blood pressure (BP) is still not effectively controlled in about 20–30% of hypertensive patients. Therefore, it is imperative to develop new treatments for hypertension. Veratrum alkaloids were once used for the clinical treatment of hypertension, the mechanism of which is still unclear. It was gradually phased out due to adverse reactions. This study aimed to investigate the short-term and long-term hypotensive profiles of different components of Veratrum alkaloids in spontaneously hypertensive rats (SHRs) to unveil their mechanisms of action. Total Veratrum alkaloid (V), component A (A), and veratramine (M) quickly decreased BP within 30 min of treatment, reduced renal and cardiovascular damage, and improved relevant biochemical indicators (nitric oxide [NO], endothelin-1 [ET-1], angiotensin II [Ang II)], noradrenaline [NE], etc) in SHRs to delay stroke occurrence. Thereinto, A exhibited excellent protective effects in cardiovascular disease. The metabolomic profiles of SHRs treated with V, A, and M were significantly different from those of SHRs treated with vehicle. Thirteen metabolites were identified as potential pharmacodynamic biomarkers. Through Kyoto Encyclopedia of Genes and Genomes analysis, V, A, and M-induced hypotension was mainly related to alterations in nicotinate and nicotinamide metabolism, GABAergic synapses, linoleic acid metabolism, ketone body synthesis and degradation, arginine and proline metabolism, and urea cycle, of which nicotinate and nicotinamide metabolism was the key metabolic pathway to relieve hypertension. This work shows that A is an effective and promising antihypertensive agent for hypertension treatment to reduce BP and hypertensive target organ damage, which is mainly mediated through modulating nicotinate and nicotinamide metabolism, RAS, and NO–ET homeostasis. 

Antihypertensive activity of different components of Veratrum alkaloids through metabonomic data analysis.

Intelligent recognition of milling tool wear status based on variational auto-encoder and extreme learning machine

Advances of Veratrum nigrum L. Steroid Alkaloids

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Intelligent recognition of milling tool wear status based on variational auto-encoder and extreme learning machine

Advances of Veratrum nigrum L. Steroid Alkaloids

Intelligent recognition of milling tool wear status based on variational auto-encoder and extreme learning machine