This thesis explored applications of the new emerging techniques of artificial intelligence and deep learning (neural networks in particular) for predictive maintenance, diagnostics and prognostics. Many neural architectures such as fully-connected, convolutional and recurrent neural networks were developed and tested on public datasets such as NASA C-MAPSS, Case Western Reserve University Bearings and FEMTO Bearings datasets to diagnose equipment health state and/or predict the remaining useful life (RUL) before breakdown. Many data processing and feature extraction procedures were used in combination with deep learning techniques such as dimensionality reduction (Principal Component Analysis) and signal processing (Fourier and Wavelet analyses) in order to create more meaningful and robust features to use as an input for neural networks architectures. This thesis also explored the potential use of these techniques in predictive maintenance within oil rigs for monitoring oilfield critical equipment in order to reduce unpredicted downtime and maintenance costs.

翻译：本论文探讨了人工智能与深度学习（特别是神经网络）等新兴技术在预测性维护、诊断与预后中的应用。研究开发并测试了多种神经网络架构（如全连接网络、卷积神经网络和循环神经网络），并在公开数据集（包括NASA C-MAPSS、凯斯西储大学轴承数据集及FEMTO轴承数据集）上验证其诊断设备健康状态和/或预测设备故障前的剩余使用寿命（RUL）的能力。结合降维（主成分分析）与信号处理（傅里叶和小波分析）等数据预处理与特征提取方法，构建更具意义且鲁棒的输入特征用于神经网络架构。此外，本文还探讨了上述技术在石油钻井平台预测性维护中的潜在应用，通过监测油田关键设备以减少非计划停机时间和维护成本。