| 1. |
- Aranda, Jesus Javier Lechuga
(författare)
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Towards Self-Powered Devices Via Pressure Fluctuation Energy Harvesters
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The growing interest in the Internet of Things has created a need for wireless sensing systems for industrial and consumer applications. In hydraulic systems, a widely used method of power transmission in industry, wireless condition monitoring can lead to reduced maintenance costs and increase the capacity for sensor deployment. A major problem with the adoption of wireless sensors is the battery dependence of current technologies. Energy harvesting from pressure fluctuations in hydraulic systems can serve as an alternative power supply and enable self-powered devices. Energy harvesting from pressure fluctuations is the process of converting small pressure fluctuations in hydraulic fluid into a regulated energy supply to power low power electronics. Previous studies have shown the feasibility of pressure fluctuation harvesting. However, for the development of self-powered sensor systems, the methods and techniques for converting pressure fluctuations into electrical energy should be further investigated.This thesis explores the methods, limitations, opportunities and trade-offs involved in the development of pressure fluctuation energy harvesters in the context of self-powered wireless devices. The focus is on exploring and characterizing the various mechanisms required to convert pressure fluctuations into electrical energy. In this work, an energy harvesting device consisting of a fluid-to-mechanical interface, an acoustic resonator, a piezoelectric stack, and an interface circuit is proposed and evaluated. Simulations and experimental analysis were used to analyse these different components for excitation relevant to hydraulic motors.The results of this work provide new insights into the development of power supplies for self-powered sensors for hydraulic systems using pressure fluctuation energy harvesters. It is shown that with the introduction of the space coiling resonator for pressure fluctuation amplification and a detailed analysis of the fluid interface and power conditioning circuits, the understanding of the design and optimization of efficient pressure fluctuation energy harvesters is further advanced.
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| 2. |
- Ma, Xinyu
(författare)
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Power Estimation for Indoor Light Energy Harvesting
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The growing popularity of indoor light energy harvesting for wireless sensor systems and low-power electronics has created a demand for systematic power estimation methods for different lighting conditions. Although existing research has recognized the critical role played by the spectral information on the output power of a photovoltaic cell, power estimation methods have rarely considered it. The vast majority of studies on the power estimation method in the past few years have focused on the conventional diode model, and even though scaling the parameters to other light conditions seems plausible, it is sometimes problematic to interpret the physical meanings of some parameters from theory. Therefore, a systematic investigation of the light condition characterization and PV cell modeling is fundamental to appropriately estimate the available light energy of an indoor environment. The power estimation method proposed in this thesis takes both spectral and intensity information into account and provides a data-driven approach to solve the scaling problem. We use low-cost sensors to measure spectral information and select an appropriate device model based on the classification of the light source. The evaluation results for both lab and real-world light conditions show that the proposed method achieves sufficient accuracy. This study provides new insights into the indoor light energy harvesting system design and makes a contribution to research on available energy estimation of the ambient environment.
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| 3. |
- Tran, Thanh
(författare)
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Enhancing Machine Failure Detection with Artificial Intelligence and sound Analysis
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The detection of damage or abnormal behavior in machines is critical in industry, as it allows faulty components to be detected and repaired as early as possible, reducing downtime and minimizing operating and personnel costs. However, manual detection of machine fault sounds is economically inefficient and labor-intensive. While prior research has identified various methods to detect failures in drill machines using vibration or sound signals, there remain significant challenges. Most previous research in this field has used manual feature extraction and selection, which can be tedious and biased. Recent studies have used LSTM, end-to-end 1D CNN, and 2D CNN as classifiers, but these have limited accuracy for machine failure detection. Additionally, machine failure is rare in the data, and sounds in the real-world dataset have complex waveforms that are a combination of noise and sound.To address these challenges, this thesis proposes modern artificial intelligence methods for the detection of drill failures using image representations of sound signals (Mel spectrograms and log-Mel spectrograms) and 2-D convolutional neural networks (2D-CNN) for feature extraction. The proposed models use conventional machine learning classifiers (KNN, SVM, and linear discriminant) or a recurrent neural network (long short-term memory) to classify three classes in the dataset (anomalous sounds, normal sounds, and irrelevant sounds). For using conventional machine learning methods as classifiers, pre-trained VGG19 is used to extract features, and neighborhood component analysis (NCA) is used for feature selection. For using LSTM, a small 2D-CNN is proposed to extract features, and an attention layer after LSTM focuses on the anomaly of the sound when the drill changes from normal to the broken state. The findings allow for better anomaly detection in drill machines and the development of a more cost-effective system that can be applied to a small dataset.Additionally, I also present a case study that advocates for the use of deep learning-based machine failure detection systems. We focus on a small drill sound dataset from Valmet AB, a company that supplies equipment and processes for biofuel production. The dataset consists of 134 sounds that have been categorized as "Anomaly" and "Normal" recorded from a drilling machine. However, using deep learning models for detecting failure drills on such a small sound dataset is typically unsuccessful. To address this problem, we propose using a variational autoencoder (VAE) to augment the small dataset. We generated new sounds by synthesizing them from the original sounds in the dataset using the VAE. The augmented dataset was then pre-processed using a high-pass filter with a passband frequency of 1000 Hz and a low-pass filter with a passband frequency of 22,000 Hz, before being transformed into Mel spectrograms. We trained a pre-trained 2D-CNN Alexnet using these Mel spectrograms. We found that using the augmented dataset enhanced the classification results of the CNN model by 6.62% compared to using the original dataset (94.12% when trained on the augmented dataset versus 87.5% when trained on the original dataset). Our study demonstrates the effectiveness of using a VAE to augment a small sound dataset for training deep learning models for machine failure detection.Background noise and acoustic noise in sounds can affect the accuracy of the classification system. To improve the sound classification application's accuracy, a sound separation method using short-time Fourier transform (STFT) frames with overlapped content is proposed. Unlike traditional STFT conversion, in which every sound is converted into one image, the signal is split into many STFT frames, improving the accuracy of model prediction by increasing the variability of the data. Images of these frames are separated into clean and noisy ones and subsequently fed into a pre-trained CNN for classification, making the classifier robust to noise. The efficiency of the proposed method is demonstrated using the FSDNoisy18k dataset, where 94.14 percent of 21 classes were classified successfully, including 20 classes of sound events and a noisy class.
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| 4. |
- Xu, Ye
(författare)
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Rotational Electromagnetic Energy Harvesting Through Variable Reluctance
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Rotating components are found in a majority of modern industrial applications.As key parts for machinery operations, rotating components need tobe monitored in order to detect and prevent machine failures. This requiresvarious sensor devices, which are electronic systems that detect and respondto physical quantities obtained from rotating components or their surroundingenvironments.With the rapid development of semiconductor technology, sensor deviceshave low power consumption, enabling energy harvesting to remove the dependenceon battery or wired power solutions and thus leading to self-poweredsensing applications. The kinetic energy of rotating components provides aubiquitous and stable energy source that can be exploited, resulting in rotationalenergy harvesting as a promising solution to produce electrical powerfor sensor devices.The research in this thesis focuses on the rotational energy harvesting bymeans of variable reluctance (VR) principle. In the literature, despite VR energyharvesting being a suitable candidate for the conversion of rotary kineticmotion, a comprehensive study on this energy harvesting system is still lacking.Moreover, low rotational speeds lead to a low level of extracted energyand negative mechanical effects on the rotary host which makes the deploymentof a VR energy harvesting to achieve a self-powered sensing applicationin rotating environment challenging, requiring a closer investigation onthe design and implementation. Based on theoretical analyses and numericalsimulations, combined with experimental validations, this research expandson the study of VR energy harvesting by exploring various structural designs,introducing a systematical optimization, demonstrating a sensing application,and investigating different circuits for AC/DC energy conversion to minimizethe circuit losses. The results of this research provide a guideline for enhancingthe performance of VR energy harvesting in low-speed rotational applications,which expands the research field on energy harvesting for realizingself-powered wireless sensing systems used in rotating environments.
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