Title: Sensitivity Analysis by Differential Importance Measure for Unsupervised Fault Diagnostics

Authors: G. Floreale, P. Baraldi, X. Lu, P. Rossetti, E. Zio

Abstract: Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: 1) it can be developed using only normal condition data 2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; 3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; 4) it can be used in combination with any fault detection technique.

Title: A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks

Journal: Reliability Engineering & System Safety

Authors: Zhe Yang, Piero Baraldi, Enrico Zio

This is an open-access article that can be downloaded from the following link before February 08, 2022: https://authors.elsevier.com/a/1eHH73OQ%7EfVtoN

Abstract:

In multi-component systems, degradation, maintenance, renewal and operational mode change continuously the operating conditions. The identification of the onset of abnormal conditions from signal measurements taken in such evolving environments can be quite challenging, due to the difficulty of distinguishing the real cause of the signal variations. In this work, we present a method for fault detection in evolving environments that uses a Sparse Autoencoder-based Deep Neural Network (SAE-DNN) and a novel procedure that remarkably reduces the computational burden for setting the values of the hyperparameters. The method is applied to a synthetic case study and to a bearing vibration dataset. The results show that it is able to accurately detect faults in multi-component systems, outperforming other state-of-the-art methods.

Title: Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews

Journal: Energies

Authors: Luca Pinciroli, Piero Baraldi, Guido Ballabio, Michele Compare, Enrico Zio

This is an open-access article that can be downloaded from the following link: https://www.mdpi.com/1996-1073/14/20/6743/pdf

Abstract:

The life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible to significantly optimize operation and maintenance (O&M) by combining the (uncertain) information provided by PHM with the other factors influencing O&M activities, including the limited availability of maintenance crews, the variability of energy demand and corresponding production requests, and the long-time horizons of energy systems operation. In this work, we consider the operation and maintenance optimization of wind turbines in wind farms woth multiple crews. A new formulation of the problem as a sequential decision problem over a long-time horizon is proposed and solved by deep reinforcement learning based on proximal policy optimization. The proposed method is applied to a wind farm of 50 turbines, considering the availability of multiple maintenance crews. The optimal O&M policy found outperforms other state-of-the-art strategies, regardless of the number of available maintenance crews.