Electrolux (Italy)

In this study, we address the challenge of detecting faults in the rotation of the spray arm in non-connected domestic dishwashers using highly noisy accelerometer data from a commercial MEMS sensor. We propose a Deep Neural Network model optimized to run in an entry-level, single-core microcontroller unit (MCU) aimed at discriminating between blocked and free states of the arm component. The proposed Embedded-AI classifier, also referred to as the 1DCNN-MLP model, is verified to be suitable for the selected hardware by a Processor-in-the-loop profiler. It is intended to execute multiple times during the washing cycle to adjust appliance control and notify the customer of necessary interventions, thereby preventing cycle repetition and saving electricity, water, and time.

In this paper, we endow the model reference adaptive control (MRAC) with a novel parameter-dependent input normalization (PIN) to completely eliminate the conventional assumption of the high-frequency gain. Specifically, neither the sign nor the prior knowledge of the upper or lower bounds is required. To this end, we resort to an error augmentation together with a smart design of an adaptive law with a dead zone operation. Global stability in the mean square sense is established with the conventional proof concepts of the augmented error approach. In this way, no persistent excitation requirement is required. Although the system in question is assumed to be unity-relative-degree, the proposed technique can be easily extended to systems of arbitrary relative degrees. Finally, compared with the Nussbaum function-based methods in a numerical experiment, we show that transient behavior in our method is significantly improved.

This paper presents the application of the Design for Reliability (DfR) approach to the relay controlling the oven's grill heating element. The conventional stress-versus-strength reliability methodology is enhanced through data from connected devices and localized load measurements, enabling the derivation of a detailed load spectrum for the relay. This approach increases the accuracy of reliability predictions, eliminating the need for success-run tests typical of the test-to-pass approach.

In this work, we propose a direct-adaptive MRAC for relative-degree-unity SISO systems with unknown control direction. The proposed scheme, employing an original construction of the control law and the use of an adaptive observer, achieves the long-searched objective of injecting, through the input, the unmeasurable derivative of the output error. The output derivative injection is performed by a smart construction of the control input that features a Parameter-dependent Input Normalization (PIN). The PIN scheme does not make use of Nussbaum functions usually invoked in the direct-adaptive setting, does not require persistence of excitation of indirect adaptive schemes, does not require switching between multiple models, does not suffer from singularities and does not require to know a-priori bounds on the norm of the high-frequency gain and on the parameters. Effectiveness of the algorithm is illustrated by a numerical example.

Abstract Background and ObjectivesIn catering environments, subsequent batches of pasta are cooked in the same water to save water and energy. This results in formation of a foam layer made up by solids leached from the pasta. Here, the effect of cooking up to 13 subsequent batches on spaghetti cooking and foaming of the cooking water was investigated. Cooking at acidic pH was also studied for a single batch.FindingsWater uptake by dry spaghetti decreases with batch number. The increasingly lower mobility in the network of swollen starch and polymerized protein network reduces the extent to which albumins and globulins are leached out. Evidently, the solid content in the cooking water increases with batch number while its pH drops. Separation of pasta fragments from the cooking water does not significantly affect (p > 0.05) the foam stability.Significance and NoveltyThis study provides additional insights in the composition and foaming properties during the continuous cooking process of pasta.

Disturbance rejection for uncertain systems is a longstanding problem of both theoretical and practical importance. In this paper, we propose a novel switching-based Adaptive Feedforward Controller (AFC) to remove a priori information on the sign of the plant transfer function at the frequency of interest (the so-called SPR-like condition), which is an undesirable but inevitable requirement for the existing AFC approaches. A distinctive feature of the work presented herein is improving the transient behavior by adopting a new switching mechanism based on an unnormalized adaptation law. Furthermore, the dimension of the overall controller is kept relatively low regardless of the number of candidate controllers. Boundedness of the trajectory of the closed-loop system and asymptotic zeroing of the output are rigorously proved. The effectiveness of the proposed technique is illustrated by numerical examples.

The aim of this paper is to analyse the fatigue behaviour of an additively manufactured (AM) short glass fiber reinforced polymer. The material under investigation is a recycled mineral-filled polypropylene reinforced by means of short glass fibers. The specimens have been produced by using a Pellet-based Additive Manufacturing (PAM) system, which is basically a Fused Deposition Modelling (FDM) process in which the feedstock consists of pellets. The three infill patterns most commonly used in 3D printing were selected to produce dog-bone shaped specimens oriented with their axes parallel to the platform (i.e. orthogonal to the build direction). The fatigue behaviour of recycled 3D printed specimens was then compared to the results obtained from specimens made of the same material produced by injection moulding (IM). The effect of the recycling was studied only for the IM specimens by testing both the virgin and recycled compound. Finally, preliminary analyses of the macroscopic damage mechanisms were done by observing the fracture paths after fatigue failure.

We consider the problem of rejection of a sinusoidal disturbance of unknown frequency, phase and magnitude acting on an uncertain internally stable SISO linear system. We present a solution that extends our previous work on adaptive feedforward control of uncertain systems by disposing of the need to know the frequency of the harmonic disturbance. The proposed methodology reposes upon a switching-based combination of an adaptive feedforward control algorithm and a deadbeat frequency estimator. The method accounts for the presence of bounded sensor noise as well as imprecise frequency estimation; it is shown that the regulation error is bounded by a function of the norm of the noise that depends on the choice of the controller and the estimator gains.