There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining processes. Traditionally, for the purpose of predicting tool wear conditions in machining, mathematical models have been developed to extract the information from the signal of sensors attached to the machines. To reduce the complexity of developing physical models, where an in-depth knowledge of the system being modelled is required, the current trend is to use machine-learning (ML) models based on data from the tool wear. The acoustic emission (AE) technique has been widely used to capture data from and understand the real-time condition of industrial assets such as cutting tools. However, AE signal interpretation and processing is rather complex. One of the most common features extracted from AE signals to predict the tool wear is the counts parameter, defined as the number of times that the amplitude of the signal exceeds a predefined threshold. A recurrent problem of this feature is to define the adequate threshold to obtain consistent wear prediction. Additionally, AE signal bandwidth is rather wide, and the selection of the optimum frequencies band for feature extraction has been pointed out as critical and complex by many authors. To overcome these problems, this paper proposes a methodology that applies multi-threshold count feature extraction at multiresolution level using wavelet packet transform, which extracts a redundant and non-optimal feature map from the AE signal. Next, recursive feature elimination is performed to reduce and optimize the vast number of predicting features generated in the previous step, and random forests regression provides the estimated tool wear. The methodology presented was tested using data captured when turning 19NiMoCr6 steel under pre-established cutting conditions. The results obtained were compared with several ML algorithms such as k-nearest neighbors, support vector machines, artificial neural networks and decision trees. Experimental results show that the proposed method can reduce the predicted root mean squared error by 36.53%.

Nowadays, new challenges around increasing production quality and productivity, and decreasing energy consumption, are growing in the manufacturing industry. In order to tackle these challenges, it is of vital importance to monitor the health of critical components. In the machine tool sector, one of the main aspects is to monitor the wear of the cutting tools, as it affects directly to the fulfillment of tolerances, production of scrap, energy consumption, etc. Besides, the prediction of the remaining useful life (RUL) of the cutting tools, which is related to their wear level, is gaining more importance in the field of predictive maintenance, being that prediction is a crucial point for an improvement of the quality of the cutting process. Unlike monitoring the current health of the cutting tools in real time, as tool wear diagnosis does, RUL prediction allows to know when the tool will end its useful life. This is a key factor since it allows optimizing the planning of maintenance strategies. Moreover, a substantial number of signals can be captured from machine tools, but not all of them perform as optimum predictors for tool RUL. Thus, this paper focuses on RUL and has two main objectives. First, to evaluate the optimum signals for RUL prediction, a substantial number of them were captured in a turning process and investigated by using recursive feature elimination (RFE). Second, the use of bidirectional recurrent neural networks (BRNN) as regressive models to predict the RUL of cutting tools in machining operations using the investigated optimum signals is investigated. The results are compared to traditional machine learning (ML) models and convolutional neural networks (CNN). The results show that among all the signals captured, the root mean squared (RMS) parameter of the forward force ( $${F}_{y}$$F y ) is the optimum for RUL prediction. As well, the bidirectional long-short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU), which are two types of BRNN, along with the RMS of $${F}_{y}$$F y signal, achieved the lowest root mean squared error (RMSE) for tool RUL, being also computationally the most demanding ones.

This article performs a thorough evaluation of different environmental and electrical waveform characteristics affecting PDIV on electric motor winding insulation. Temperature received special attention, as it clearly affects PDIV. Additionally, the PDIV models found in the literature were reviewed. Considering that the winding of the electric motor can reach 180 ${}^{°}$C during operation, the experiments were performed at high temperatures in order to evaluate the performance of PDIV models. Several models were studied regarding their accuracy at high temperatures and the advantages and disadvantages of each one were identified. Based on the analysis, a simple analytical model to estimate the PDIV of twisted pairs depending on the temperature was proposed. All in all, the proposed model was the best compromise between computational requirements and PDIV estimation accuracy at high temperatures. Finally, future lines were identified. Further studies are necessary that consider the humidity, rise time, and pulse width effect on PDIV. Moreover, new models regarding both, environmental and waveform characteristics are necessary to accurately estimate PDIV. Extended volume–time theory seems to be a good basis for that.

Due to their particular structure, switched-flux permanent magnet machines have become a very interesting alternative for many applications. This is why some recent studies have been focused in the understanding of the operating mechanism of these machines via the MMF-permeance modelling. However, the models that can be found in the literature make some simplifications that reduce their accuracy when predicting the performance of switched-flux machines. For example, the models that can be found in the literature are commonly not precise enough for machines with a wide slot, because the influence of the modulator of the primary side of the machine is neglected. In this article, a precise analytical model is developed for a 6/13 C-Core switched-flux machine via a combination of a magnetic equivalent circuit and a MMF-permeance model. The model is based on the magnetic field modulation principle. The analytical model is used to explain the flux focusing effect and the force generation mechanism of switched-flux machines. A new concept of PM field harmonic efficiency ratio is used to identify the most efficient PM field harmonics of 2 switched-flux machines. The precision of the model is validated via 2D and 3D Finite Element Method simulations, and experimental measurements that were obtained with a linear machine prototype. The results show that the model can predict the performance of switched-flux machines with a high accuracy level.

This paper presents an electromagnetic design alternative using the so-called banana-shaped magnets, achieving magnet weight reduction of 21.82% for the same performance as the baseline motor in the case study. Typically, parallel magnetization is used in this topology because it provides the manufacturing advantage of not building a specific magnetizing device required to achieve radial magnetization. Therefore, as its main contribution, this paper proposes using tilted magnetization, demonstrating that the performance is better than the conventional way of applying parallel magnetization in banana-shaped motors but still using parallel magnetization. Because of the performance improvement for the same motor structure, magnet weight reduction is possible for a new design with the same performance as the baseline motor. In addition, this paper presents the workflow followed to obtain the less rare-earth design motor candidate using tilted magnetization.