The automotive industry is becoming more dependent on sustainable and efficient systems within vehicles. With the diverse combination of conditions affecting vehicle performance, such as environmental conditions and drivers' behaviour, the interest in monitoring machine health increases. This master thesis examines the machine learning approach to sensor reconstruction of hauler engine sensors for deviation detection applications across multiple domains. A novel proposal for sequence learning and modelling was by introducing a weighted difference of sequence derivatives. Impacts of including differences of derivatives assisted the learning capabilities of sequential data for the majority of the target sensors across multiple operating domains. Robust sensor reconstruction was also examined by using inductive transfer learning with a Long Short-Term Memory-Domain Adversarial Neural Network. Obtained results implied an improvement in using the Long Short-Term Memory-Domain Adversarial Neural Network, then using a regular Long Short-Term Memory network trained on both source and target domains. Suggested methods were evaluated towards model-based performance and computational limitations. The combined aspects of model performance and system performance are used to discuss the trade-offs using each proposed method.

With the advancement of neural networks and deep learning, the complexity and size of models have increased exponentially. On the other hand, advancements of internet of things (IoT) and sensor technology have opened for many embedded machine learning applications and projects. In many of these applications, the hardware has some constraints in terms of computational and memory resources. The always increasing popularity of these applications, require shrinking and compressing neural networks in order to satisfy the requirements. The frameworks and algorithms governing the compression of a neural network are commonly referred to as pruning algorithms. In this project several pruning frameworks are applied to different neural network architectures to better understand their effect on the performance as well as the size of the model. Through experimental evaluations and analysis, this thesis provides insights into the benefits and trade-offs of pruning algorithms in terms of size and performance, shedding light on their practicality and suitability for embedded machine learning. The findings contribute to the development of more efficient and optimized neural networks for resource constrained hardware, in real-world IoT applications such as wearable technology. / Med framstegen inom neurala nätverk och djupinlärning har modellernas komplexitet och storlek ökat exponentiellt. Samtidigt har framsteg inom Internet of Things (IoT) och sensorteknik öppnat upp för många inbyggda maskininlärningsapplikationer och projekt. I många av dessa applikationer finns det begränsningar i hårdvaran avseende beräknings- och minnesresurser. Den ständigt ökande populariteten hos dessa applikationer kräver att neurala nätverk minskas och komprimeras för att uppfylla kraven. Ramverken och algoritmerna som styr komprimeringen av ett neuralt nätverk kallas vanligtvis för beskärningsalgoritmer. I detta projekt tillämpas flera beskärningsramverk på olika neurala nätverksarkitekturer för att bättre förstå deras effekt på prestanda och modellens storlek. Genom experimentella utvärderingar och analys ger denna avhandling insikter om fördelarna och avvägningarna med beskärningsalgoritmer vad gäller storlek och prestanda, och belyser deras praktiska användbarhet och lämplighet för inbyggd maskininlärning. Resultaten bidrar till utvecklingen av mer effektiva och optimerade neurala nätverk för resursbegränsad hårdvara i verkliga IoT-applikationer, såsom bärbar teknik.