Pseudorandom number generators (PRNGs) are fundamental components in cryptographic algorithms. The new concept of multi-PRNG introduced in this article consists of a unique generator capable of producing multiple streams of pseudorandom numbers. Multiscroll chaotic systems are known for generating multiple scrolls within a single attractor. With the aforementioned this article introduces a novel field-programmable gate array (FPGA) implementation of a multi-PRNG based on a multiscroll chaotic memristive Hopfield neural network (MHNN). The main contribution of this work is the generation of multiple spatially dependent PRNG streams from a chaotic multiscroll system by dividing the phase space of the attractor into sub-phase spaces. Each scroll in the multiscroll attractor functions as an independent PRNG. This innovative approach to generating multiple PRNGs from multiscroll chaotic systems is unprecedented in the existing literature. The 5-D MHNN chaotic model used in this work employs hyperbolic tangent and sine functions, which were implemented through a hardware-efficient CORDIC approach. Besides, The FPGA implementation to produce the chaotic time series leverages the Euler method with 32-bit fixed-point arithmetic, selected for its simplicity and low resource utilization. Finally, The randomness of the binary sequences produced by the multi-PRNG is rigorously validated using the NIST SP 800-22a and TestU01 suites, confirming their potential for cryptographic applications.

Induction motors are commonly affected by damaged rotor bars. To avoid the consequences that this fault may induce, new broken bar fault detection is being proposed, aiming to achieve a high fault detection accuracy in an automatized way. To this end, some approaches rely on neural networks or artificial intelligence algorithms. However, these methods imply a high use of computational resources. Other proposals with lower computational demand are based on a signal processing algorithm, such as the Taylor-Fourier transform, and statistical-based approaches, like kurtosis analysis, for extracting signal data distributions’ characteristics. In this work, the O-Splines of the Taylor-Fourier transform are employed to apply a bank of seven filters to the current signals of an induction motor under different damage levels and two load conditions. The Taylor-Fourier transform is an excellent feature extraction option, suitable for characterizing the current signals’ behavior under different damage and load states. Then, the kurtosis of the seven filtered signals is computed, and based on the separability of data among the healthy case and the faulted ones, one filter is selected for each load case. Finally, based on the selected filter, the amplitude estimations of the filtered signals are displayed as boxplots to identify the motor condition. The main advantage of this method is that it automatically detects the best central frequency of the filter according to the load condition based on the kurtosis, eliminating the need for prior knowledge of the load condition and the motor’s characteristics. Results suggested that this methodology can identify the presence of the broken bar fault starting from a case of 70% of damage in the bar for both 75% and 50% load conditions.

This paper presents a novel approach for early detection of bearing faults in induction motors based on chaotic behavior analysis. Two comprehensive case studies are detailed. The first study delves into an analysis utilizing an internal database containing one hundred tests for each operational condition, ensuring enough statistical information for effective fault detection. Three distinct fault types (ball damage, outer race damage, and rusted bearing) were induced to simulate real-world scenarios. The second case study focuses on vibration signals sourced from Case Western Reserve University, specifically investigating ball and inner race damage for both loaded and unloaded conditions. Both current and vibration signals were analyzed employing chaos-based density of maxima calculations. This methodology entails quantifying the peaks and evaluating the density of maxima within the motor signals for correlation length calculation, providing valuable chaotic insights for fault detection with results that yield 100% of accuracy.

The early fault detection in the rotary electrical machines,such as induction motors (IMs), has been growing in modern industry. IMs have been widely used in industrial applications due to its easy installation, reliability, and low cost. However, the increasing usage of IMs also increases the need for timely maintenance in order to ensure their operation and a longer service life. This article proposes a new diagnosis methodology based on maximal overlap discrete wavelet transform and a lightweight 1-D convolutional neural network (CNN) architecture, in order to detect mechanical and electrical faults and their combination, in adjustable speed drive (ASD)-powered IMs. Specifically, single and combined faults were studied from the next: Outer raceway bearing (mechanical), turn-to-turn short-circuit, and phase-to-ground short circuit (electrical). The presented study was developed using current signals acquired from stators of IMs of 1 hp. The current signals are measured at powered conditions introduced by a power grid with a constant frequency at 60 Hz, and an ASD at three different frequencies. The proposed diagnostic methodology reaches more than 99% of accuracy.

Walsh Hadamard Transform (WHT) is an orthogonal, symmetric, involutional, and linear operation used in data encryption, data compression, and quantum computing. The WHT belongs to a generalized class of Fourier transforms, which allows that many algorithms developed for the fast Fourier transform (FFT) work for fast WHT implementations (FWHT). This paper employs this property and uses a parallel-pipeline FFT well-known strategy for VLSI implementation to build parallel-pipeline architectures for FWHT. We apply the FFT parallel-pipeline approach on a Fast WHT and use the High-Level Synthesis (HLS) tool from Xilinx Vitis to generate an FPGA solution. We also provide an open-source code with the basic blocks to build any model with any parallelization level. The parallel-pipeline proposed solutions achieve a latency reduction of up to 3.57% compared to a pipeline approach on a 256-long signal using 32 bit floating-point numbers.

Induction machines are widely used in several industries around the world; their robust design allows them to operate even under nonoptimal conditions; the nonoptimal operation can reduce the machine lifetime depending on the anomaly magnitude; this leads to a loss of process efficiency, which eventually generates a considerable operational costs increment. Monitoring methods, that allow an early fault detection, are getting developed currently; these methods are focused on the fault detection of the main components of the machine; one of them is the bearing fault detection that can be obtained through the phase current signal analysis. In this article, three types of goodness-of-fit test are studied; in these methods, the motor current signature and the motor square current signature are analyzed. Furthermore, three types of bearing damage are presented and studied; the damages studied are: single point damage (bearing outer-race damage and bearing ball damage), and distributed damage (corrosion damage). The induction machine signals, when working with the damages mentioned before, are measured at two powering conditions: power grid sourced (at 60 Hz constant frequency), and adjustable speed drive (at six operating frequencies).

Surrogate modeling techniques are of particular interest for engineering design when high-fidelity, thus expensive analysis codes are used. They provide sufficiently accurate solutions by using numeric approximation models. Recently, surrogates have been employed adding engineering and expert knowledge to improve the accuracy and the convergence of the algorithm. This paper proposes a granular-surrogate model, which in turns provides a structure to extract and represent some knowledge with fuzzy logic. The extracted rule-based understanding of the granule's activity allows us to design two fuzzy controllers to manage the parameters update, providing a self-adaptive granular surrogate model according to the characteristics of the function handled. With this proposal, we are changing from a data-driven surrogate to a knowledge-based one, showing the effectiveness of the algorithm in standard benchmarks.

Genetic Algorithms are widely used in optimization and search problems trying to find the more useful and better solutions according to one or several objectives. However, due to the high dimensionality in the space of solutions, this heuristic algorithm is computationally expensive. A viable alternative that produces acceptable results, specially in engineering problems, is based in the idea of alternative models that reflects approximate solutions, and it is called a surrogate model. In this paper, the employed surrogate model is based on granular computing, an emerged concept from fuzzy theory, that deals with grouping the solutions in the search space according to some specific similarities. We present a detail algorithm to construct such granular entities, and to find the optimal adaption process routed to not only avoid excessive fitness evaluations but also to obtain a better convergence of the algorithm. The obtained results on traditional benchmark functions show satisfactory improvements to such heuristic process.

Machine monitoring is one of the major concerns in modern industry in order to guarantee the overall efficiency during the production process. Several monitoring techniques for machinery failure detection have been developed, being vibration analysis one of the most important techniques. The typical equipment used for vibration analysis is a general purpose single channel spectrum analyzer that most of the cases is not well suited for the specific task and lacks from the capability of simultaneous multiple channel analysis and it is not specifically designed for vibration analysis. The contribution of this work is to present the development of a low-cost FPGA based 3-axis simultaneous vibration analyzer for embedded machinery monitoring with the novelty of a post-processing stage that can be designed and implemented into the same FPGA for automatic on-line detection of specific machinery failures thanks to its reconfigurability. The vibration analyzer has three stages: vibration monitoring with a MEMS accelerometer as sensor, three parallel 1024-point FFT cores and one post-processor for the analysis of the specific vibration related failure, which can be reconfigured to attend the specific task. One of the most important failures in induction motors is the broken bar condition and the developed vibration analyzer was tested to detect this condition on several motors for different failure severities, giving good detection results. Other vibration analysis can be performed by the three channel FFT cores with the reconfiguration of the post-processing unit to detect or enhance a specific characteristic under study

Preventive maintenance is one of the major concerns in modern industry where failure detection on motors increases the useful life cycle on the machinery. Bar breakage is one of the most common failures on motors and their condition monitoring is a mandatory task for industries. Previous works for bar breakage detection are based on off-line current or vibration analysis through the spectrum, but their detectability is compromised under certain operating conditions. The novelty of this work is the proposal of a correlation algorithm that combines current and vibration spectra to enhance detectability where other works fail. Current and vibration monitoring are non-invasive methods and they are preferred for on-line analysis. The proposed correlation between current and vibration allows to detect the broken bar condition on induction motors when no load is applied to the motor, which is the condition where other reported works fail to detect. The contribution of this work is that the proposed algorithm, though complex in computational load, is implemented into a low-cost FPGA (Spartan-3 XC3S1000) that gives a special purpose SOC solution for on-line operation, thanks to the development of a special purpose hardware signal processing unit. The developed FPGA based algorithm was tested for different bar breakage conditions in the induction motors and with several loads, giving as a result a better detectability for the motor failure under conditions where other methodologies fail, with the additional advantage of being a low-cost SOC solution for on-line detection