On the economics of asset management

Asset Management is about realizing value from physical assets. To do this, money has to be invested in physical assets (purchase, maintenance, consumables, etc.) thus producing a specific technical performance for each asset over its lifecycle. The technical performance then allows to realize value for the owner. This can be either a monetary value (e.g. for a production firm that can sell products) or a non-monetary value (e.g. for a utility that can provide a reliable electricity supply). We examine the nature of physical assets as investment objects and derive some conclusions on optimal investment strategies. We develop a general model for physical assets as investment objects, simultaneously describing both the life cycle cost structure and the value realization under different operational policies. We show that physical assets are investments that have properties which distinguish them from classical financial investments such as bonds, stocks, or the like. In particular, the non-proportional relation of investment and value creation has important implications for the derivation of optimal investment strategies. We apply the framework to the problem of budget allocation in a portfolio of physical assets. The model allows the calculation of the optimal allocation such that the total value creation is maximized. It turns out that the solution is similar to the well-known Equimarginal Principle. We also re-examine a classical optimization problem from the maintenance literature and show that the classical solution may lead to wrong results because assets are regarded in isolation instead as part of a larger system of investment options. Since our approach combines both the cost and the value generation aspect of physical assets, and includes operational lifecycle policy decisions, it could form the conceptual basis for a new approach to asset management

Anlagenbewirtschaftung und Nutzenmaximierung

Mit dem verfügbaren Budget möglichst viel aus technischen Anlagen herausholen ist das Ziel des modernen Asset Managements. In Zusammenarbeit mit der Zürcher Hochschule für Angewandte Wissenschaften ZHAW wurde eine strukturierte Methodik für das Asset Management entwickelt, die es erlaubt, dieses Ziel in der Praxis zu erreichen. Erste Anwendungen zeigen ein langfristig gesteigertes Nutzenpotential von 20-30%

A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data

Anomaly detection (AD) tasks have been solved using machine learning algorithms in various domains and applications. The great majority of these algorithms use normal data to train a residual-based model, and assign anomaly scores to unseen samples based on their dissimilarity with the learned normal regime. The underlying assumption of these approaches is that anomaly-free data is available for training. This is, however, often not the case in real-world operational settings, where the training data may be contaminated with a certain fraction of abnormal samples. Training with contaminated data, in turn, inevitably leads to a deteriorated AD performance of the residual-based algorithms. In this paper we introduce a framework for a fully unsupervised refinement of contaminated training data for AD tasks. The framework is generic and can be applied to any residual-based machine learning model. We demonstrate the application of the framework to two public datasets of multivariate time series machine data from different application fields. We show its clear superiority over the naive approach of training with contaminated data without refinement. Moreover, we compare it to the ideal, unrealistic reference in which anomaly-free data would be available for training. Since the approach exploits information from the anomalies, and not only from the normal regime, it is comparable and often outperforms the ideal baseline as well

Risk Based Maintenance (RBM) : Minimierung der Nutzerrisiken und Betriebskosten mit einer risikobasierten Methode für den Unterhalt der BSA

In Strassentunneln werden verschiedene Betriebs- und Sicherheitsausrüstungen (BSA) installiert, um einen sicheren Betrieb zu gewährleisten. Störungen oder Ausfälle dieser Systeme erzeugen ein Risiko für die Verkehrsteilnehmer, den Betreiber und die Umwelt. Damit die BSA möglichst zuverlässig funktionieren, werden regelmässige Wartungs- und Instandhaltungsarbeiten durchgeführt. Diese Arbeiten sind mit Kosten verbunden, sorgen im Gegenzug im Optimalfall jedoch für ein tieferes Risiko. Um ein möglichst tiefes Gesamtrisiko zu erzielen, muss das zur Verfügung stehende Budget optimal eingesetzt werden. Dazu muss untersucht werden, wie stark die einzelnen Wartungs- und Instandhaltungstätigkeiten das Gesamtrisiko beeinflussen, und wie auf dieser Grundlage die optimale Kombination von Tätigkeiten (Instandhaltungsstrategie) gefunden werden kann, die für ein gegebenes Budget eine maximale Risikoreduktion erzeugen. In diesem Dokument wird eine Methodik zur Entwicklung einer risikobasierten Instandhaltung bei Betriebs- und Sicherheitsausrüstungen beschrieben. Mithilfe dieser Methodik können unterschiedliche Instandhaltungsstrategien für einzelne Anlagen hinsichtlich Kosten und Risiko vergleichbar gemacht werden, um so die Basis für eine risikobasierte Instandhaltung zu schaffen. Darüber hinaus erlaubt die Methodik, für ein Anlagenportfolio aus vielen Anlagen eine optimale Gesamt-Instandhaltungsstrategie zu bestimmen, die entweder bei gegebenem Gesamtbudget ein minimales Gesamtrisiko erzeugt, oder bei einem vorgegebenen Gesamtrisiko ein minimales Budget benötigt. Dabei werden im Wesentlichen folgende Kernpunkte behandelt: - Vorgehen zur Identifikation der Instandhaltungstätigkeiten und Beurteilung/Abschätzung ihres Optimierungspotentials hinsichtlich ihrer Wiederholungsfrequenz. - Methodik und Modellierungsgrundsätze zu einer systematischen und strukturierten Berechnung von Risiko und Kosten der Instandhaltungstätigkeiten. - Einheitlicher Vergleich des Risikoreduktionbeitrages von verschiedenen Instandhaltungstätigkeiten an unterschiedlichen Betriebs- und Sicherheitsausrüstungen. - Optimierungsverfahren hinsichtlich Risiko und Kosten über ein Portfolio von Betriebs- und Sicherheitsausrüstungen bzw. deren Wartungs- und Instandhaltungstätigkeiten, um damit eine optimale Gesamt-Strategie abzuleiten. Im vorliegendem Forschungsprojekt wurde eine praxistaugliche Methodik zur Anwendung der risikobasierten Instandhaltung entwickelt. In 6 standardisierten Phasen kann der Zusammenhang zwischen gewählter Instandhaltungsstrategie (Tätigkeiten, Häufigkeiten), deren Kosten und resultierendes Risiko einer Anlage ermittelt werden. Mit dieser Methode konnte erstmals im BSA-Kontext quantitativ der Zusammenhang zwischen den Ausgaben für Wartung und Instandhaltung und dem daraus resultierenden Risiko ermittelt werden. Diese Standardisierung erlaubt es einerseits für eine gegebene Risikoschranke (akzeptiertes Risiko) die minimal notwendigen Kosten zu ermitteln. Umgekehrt kann für ein gegebenes Gesamtbudget das minimale damit erreichbare Risiko ermittelt und die dazugehörige Instandhaltungsstrategie identifiziert werden. Dies ist möglich auf der Ebene einer einzelnen Anlage oder Anlagenkategorie, für alle Anlagen eines oder mehrerer Tunnel, aber auch auf der Ebene des Gesamtportfolios aller in der Schweiz installierten Anlagen. Die Methodik wurde an den Pilotanwendungen Adaptationsbeleuchtung, Lüftung, VMSystem, Brandmeldeanlage und Notstromanlage durchgeführt. Die Resultate zeigen, dass durch eine Änderung der Instandhaltungsstrategie für die innerhalb des Forschungsprojekts modellierten Wartungstätigkeiten sowohl die Gesamtkosten als auch das Gesamtrisiko um bis zu rund 20% reduziert werden können. Ein weiterer Vorteil dieser standardisierten Methode ist, dass durch die konsequente Verknüpfung von Instandhaltungstätigkeiten und den zugehörigen Risiken eine einheitliche Wissenslage über die positiven Wirkungen der verschiedenen Tätigkeiten entsteht. Dies wird zu einer Harmonisierung und Optimierung der Instandhaltungsaktivitäten der verschiedenen Betreiber führen und kann das lokal vorhandene Expertenwissen in optimaler Weise für das Gesamtportfolio der Schweizer BSA nutzbar machen. Eine Einschätzung des Potentials bei einer schweizweiten Einführung zeigt auf, dass die jährlichen Wartungskosten um rund 2.8 Mio. CHF gesenkt werden könnten, ohne das Gesamtrisiko über alle Tunnel zu erhöhen. Gesehen auf die gesamten Wartungskosten in Tunneln bedeutet dies eine mögliche Kostenreduktion von bis zu 12%

Uncertainty informed anomaly scores with deep learning : robust fault detection with limited data

Best Paper Award Lizenzangabe: CC BY 3.0 United StatesQuantifying the predictive uncertainty of a model is an important ingredient in data-driven decision making. Uncertainty quantification has been gaining interest especially for deep learning models, which are often hard to justify or explain. Various techniques for deep learning based uncertainty estimates have been developed primarily for image classification and segmentation, but also for regression and forecasting tasks. Uncertainty quantification for anomaly detection tasks is still rather limited for image data and has not yet been demonstrated for machine fault detection in PHM applications. In this paper we suggest an approach to derive an uncertainty-informed anomaly score for regression models trained with normal data only. The score is derived using a deep ensemble of probabilistic neural networks for uncertainty quantification. Using an example of wind-turbine fault detection, we demonstrate the superiority of the uncertainty-informed anomaly score over the conventional score. The advantage is particularly clear in an "out-of-distribution" scenario, in which the model is trained with limited data which does not represent all normal regimes that are observed during model deployment

Physics-informed machine learning for predictive maintenance : applied use-cases

The combination of physics and engineering information with data-driven methods like machine learning (ML) and deep learning is gaining attention in various research fields. One of the promising practical applications of such hybrid methods is for supporting maintenance decision making in the form of condition-based and predictive maintenance. In this paper we focus on the potential of physics-informed data augmentation for ML algorithms. We demonstrate possible implementations of the concept using three use cases, differing in their technical systems, their algorithms and their tasks ranging from anomaly detection, through fault diagnostics up to prognostics of the remaining useful life. We elaborate on the benefits and prerequisites of each technique and provide guidelines for future practical implementations in other systems

Physics informed deep learning for tracker fault detection in photovoltaic power plants

One of the main challenges for fault detection in commercial fleets of machines is the lack of annotated data from the faulty condition. The use of supervised algorithms for anomaly detection or fault diagnosis is often unrealistic in this case. One approach to overcome this challenge is to augment the available normal data by generating synthetic anomalous data that represents faulty conditions. In this paper we apply this approach to the detection of faults in the tracking system of solar panels in utility-scale photovoltaic (PV) power plants. We develop a physical model in order to augment the training data for a deep convolutional neural network. We show that the physics informed learning algorithm is capable of detecting faults in an accurate and robust manner under diverse weather conditions, outperforming a purely data-driven approach. Developing and testing the algorithm with real operational data ensures its efficient deployment for PV power plants that are monitored at string level. This in turn enables the early detection of root causes for power losses, thereby contributing to the accelerated adoption of solar energy at utility scale

Cross-turbine training of convolutional neural networks for SCADA-based fault detection in wind turbines

Machine learning algorithms for early fault detection of wind turbines using 10-minute SCADA data are attracting attention in the wind energy community due to their cost-effectiveness. It has been recently shown that convolutional neural networks (CNNs) can significantly improve the performance of such algorithms. One practical aspect in the deployment of these algorithms is that they require a large amount of historical SCADA data for training. These are not always available, for example in the case of newly installed turbines. Here we suggest a cross-turbine training scheme for CNNs: we train a CNN model on a turbine with abundant data and use the trained network to detect faults in a different wind turbine for which only little data are available. We show that this scheme is able to considerably improve the fault detection performance compared to the scarce data training. Moreover, it is shown to detect faults with an accuracy and robustness which are very similar to the single-turbine scheme, in which training and detection are both done on the same turbine with a large and representative training set. We demonstrate this for two different fault types: abrupt and slowly evolving faults and perform a sensitivity analysis in order to compare the performance of the two training schemes. We show that the cross-turbine scheme works successfully also when training on turbines from another farm and with different measured variables than the target turbine

Early fault detection based on wind turbine SCADA data using convolutional neural networks

Early fault detection in wind turbines using the widely available SCADA data has been receiving growing interest due to its cost-effectiveness. As opposed to the large variety of fault detection methods based on high resolusion vibration data, the use of 10-minute SCADA data alone does not require any additional hardware or data storage solutions and would be immediately implementable in most wind farms. However, the strong variability of these data is challenging and requires significant improvements of existing methods to ensure early and reliable fault detection and isolation. Here we suggest to use Convolutional Neural Networks (CNNs) to enhance the detection accuracy and robustness. We demonstrate the superiority of the CNN model over standard fully connected neural networks (FCNN) using examples for faults with very different time dependent characteristics: an abruptly evolving and a slowly degrading fault. We show that the CNN is able to detect the faults earlier and with a higher accuracy and robustness of prediction than the FCNN model. We then extend the CNN model to a multi-output CNN (CNNm) which provides early fault detection based on a multitude of output variables simultaneously. We show that with the same training time and a similar detection quality as the single output CNN, the CNNm model is an ideal candidate for a practical and scalable fault detection algorithm based on already available 10-minute SCADA data for wind turbines

Transfer learning approaches for wind turbine fault detection using deep learning

Best Paper AwardImplementing machine learning and deep learning algorithms for wind turbine (WT) fault detection (FD) based on 10-minute SCADA data has become a relevant opportunity to reduce the operation and maintenance costs of wind farms. The development of practically implementable algorithms requires addressing the issue of their scalabililty to large wind farms. Two of the main challenges here are reducing the training times and enabling training with scarce or limited data. Both of these challenges can be addressed with the help of transfer learning (TL) methods, in which a base model is trained on a source WT and the learned knowledge is transferred to a target WT. In this paper we suggest three TL frameworks designed to transfer a semi-supervised FD task between turbines. As a base model we use a Convolutional Neural Network (CNN) which has been proven to perform well on the single turbine FD task. We test the three TL frameworks for transfer between WTs from the same farm and from different farms. We conclude that for the purpose of scaling up training for large farms, a simple TL based on linear regression transformation of the target predictions is an attractive high performance solution. For the challenging task of cross-farm TL based on scarce target data we show that a TL framework using combined linear regression and error-correction CNN outperforms the other methods. We demonstrate a scheme that enables the evaluation of different TL frameworks for FD without the need for labeled faults