Entropy is a powerful tool for the analysis of time series, as it allows describing the probability distributions of the possible state of a system, and therefore the information encoded in it. Nevertheless, important information may be codified also in the temporal dynamics, an aspect which is not usually taken into account. The idea of calculating entropy based on permutation patterns (that is, permutations defined by the order relations among values of a time series) has received a lot of attention in the last years, especially for the understanding of complex and chaotic systems. Permutation entropy directly accounts for the temporal information contained in the time series; furthermore, it has the quality of simplicity, robustness and very low computational cost. To celebrate the tenth anniversary of the original work, here we analyze the theoretical foundations of the permutation entropy, as well as the main recent applications to the analysis of economical markets and to the understanding of biomedical systems.

Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review

A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM

Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines

Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE) was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE) and multiscale entropy (MSE).

https://www.mdpi.com/1099-4300/14/8/1343/pdf

Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine

The rapid consumption of fossil fuel and increased environmental damage caused by it have given a strong impetus to the growth and development of fuel-efficient vehicles. Hybrid electric vehicles (HEVs) have evolved from their inchoate state and are proving to be a promising solution to the serious existential problem posed to the planet earth. Not only do HEVs provide better fuel economy and lower emissions satisfying environmental legislations, but also they dampen the effect of rising fuel prices on consumers. HEVs combine the drive powers of an internal combustion engine and an electrical machine. The main components of HEVs are energy storage system, motor, bidirectional converter and maximum power point trackers (MPPT, in case of solar-powered HEVs). The performance of HEVs greatly depends on these components and its architecture. This paper presents an extensive review on essential components used in HEVs such as their architectures with advantages and disadvantages, choice of bidirectional converter to obtain high efficiency, combining ultracapacitor with battery to extend the battery life, traction motors’ role and their suitability for a particular application. Inclusion of photovoltaic cell in HEVs is a fairly new concept and has been discussed in detail. Various MPPT techniques used for solar-driven HEVs are also discussed in this paper with their suitability.

/pdf/a-comprehensive-review-on-hybrid-electric-vehicles-3pxjmbw5qy.pdf

A comprehensive review on hybrid electric vehicles: architectures and components

Machine tool chatter is an unfavorable phenomenon during metal cutting, which results in heavy vibration of cutting tool. With increase in depth of cut, the cutting regime changes from chatter-free cutting to one with chatter. In this paper, we propose the use of permutation entropy (PE), a conceptually simple and computationally fast measurement to detect the onset of chatter from the time series using sound signal recorded with a unidirectional microphone. PE can efficiently distinguish the regular and complex nature of any signal and extract information about the dynamics of the process by indicating sudden change in its value. Under situations where the data sets are huge and there is no time for preprocessing and fine-tuning, PE can effectively detect dynamical changes of the system. This makes PE an ideal choice for online detection of chatter, which is not possible with other conventional nonlinear methods. In the present study, the variation of PE under two cutting conditions is analyzed. Abrupt variation in the value of PE with increase in depth of cut indicates the onset of chatter vibrations. The results are verified using frequency spectra of the signals and the nonlinear measure, normalized coarse-grained information rate (NCIR).

/pdf/permutation-entropy-based-real-time-chatter-detection-using-301ul9ctsd.pdf

Permutation entropy based real-time chatter detection using audio signal in turning process

Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis.

A passive islanding detection method based on K-means clustering and EMD of reactive power signal

Brushless DC (BLDC) motors are one of the most widely used motors, not only because of their efficiency, and torque characteristics, but also because they have the advantages of being a direct current (DC) supplied, eliminating the disadvantages of using Brushes. BLDC motors have a very wide range of speed, so speed control is a very important issue for it. Sliding mode control (SMC) is one of the popular strategies to deal with uncertain control systems. The Fuzzy Sliding Mode Controller (FSMC) combines the intelligence of a fuzzy inference system with the sliding mode controller. In this paper, an intelligent Fuzzy Sliding Mode controller for the speed control of BLDC motor is proposed. The mathematical model of the BLDC motor is developed and it is used to examine the performance of this controller. Conventionally PI controllers are used for the speed control of the BLDC motor. When Fuzzy SMC is used for the speed control of BLDC motor, the peak overshoot is completely eliminated which is 3% with PI controller. Also the rise time is reduced from 23 ms to 4 ms and the settling time is reduced from 46 ms to 4 ms by applying FMSMC. This paper emphasizes on the effectiveness of speed control of BLDC motor with Fuzzy Sliding Mode Controller and its merit over conventional PI controller.

An intelligent fuzzy sliding mode controller for a BLDC motor

https://cyberleninka.org/article/n/1431148.pdf

Usha Nair

Papers

Permutation entropy based real-time chatter detection using audio signal in turning process

Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis.

A passive islanding detection method based on K-means clustering and EMD of reactive power signal

An intelligent fuzzy sliding mode controller for a BLDC motor

Fuzzy Sliding Mode Control of a Switched Reluctance Motor