Development of an aeronautical electromechanical actuator with real time health monitoring capability

Development and implementation of EMAs has increased rapidly during the last years in the context of the “more electrical aircraft”. One of the main technical key issues for the EMA implementation is the jamming. It can appear due to metalmetal contact of load transmission (in gearboxes, bearings and ball/roller screws). This problem penalizes the reliability although with very low failure rate. To overcome this problem in aeronautical EMAs are actually several ways investigated, where one of the most attractive and with more promising is the implementation of advanced monitoring systems. This implementation of “smart” monitoring systems will imply a clear economical profit in the final product and in the complete system: envisaged benefits will be lower maintenance costs with higher reliability, instead of increasing maintenance costs and decreasing reliability for classical components without Health Monitoring. At the end, the selection of the Health Monitoring and Management system will be able to establish different levels of validation: failure detection, diagnostic and prognostic; this will provide a proactive maintenance strategy in order to replace EMA before failure. A demonstrator prototype of an innovative electromechanical actuator with real time health monitoring capability has been designed and developed by SENER. This actuator type can be taken as a reference for typical secondary control surface applications. This development is based on previous work performed by SENER in AWIATOR project where one of the tasks was the design and calculation of the new flap trailing edge with MINITEDs. In addition, this work included the supports and linkages of the current actuator to the MINITED. This compact electromechanical actuator shows innovations with respect to current state-of-the-art electrical actuators as lightness and compactness of the resulting actuator, with high power density within small dimensions. As an added value, an additional plug module is under development for real time health monitoring to detect potential working incidents: “smart actuator”. One of the additional key points will be the health management in order to solve the introduction of these systems in EMAs, and to check the compatibility with the aircraft systems

A new transformation for embedded convolutional neural network approach toward real-time servo motor overload fault-detection

Overloading in DC servo motors is a major concern in industries, as many companies face the problem of finding expert operators, and also human monitoring may not be an effective solution. Therefore, this paper proposed an embedded Artificial intelligence (AI) approach using a Convolutional Neural Network (CNN) using a new transformation to extract faults from real-time input signals without human interference. Our main purpose is to extract as many as possible features from the input signal to achieve a relaxed dataset that results in an effective but compact network to provide real-time fault detection even in a low-memory microcontroller. Besides, fault detection method a synchronous dual-motor system is also proposed to take action in faulty events. To fulfill this intention, a one-dimensional input signal from the output current of each DC servo motor is monitored and transformed into a 3d stack of data and then the CNN is implemented into the processor to detect any fault corresponding to overloading, finally experimental setup results in 99.9997% accuracy during testing for a model with nearly 8000 parameters. In addition, the proposed dual-motor system could achieve overload reduction and provide a fault-tolerant system and it is shown that this system also takes advantage of less energy consumption

Intelligent Model Based Imbalance Fault Detection and Identification System of a Turbocharger Based on Vibration Analysis

Inappropriate fault detection of turbocharger’s operating parameters has generated unnecessary economic loss due to unplanned down-time. This results to a combination of late and inaccurate diagnosis of the turbocharger faults by the employed maintenance systems. This study, used a Model-based fault diagnosis approach to identify imbalance fault in a turbocharger rotor system. In this approach, the generalized theoretical equation of motion for both healthy and faulty system models of a complete turbocharger rotor, were developed using the Finite element method. A test rig for the turbocharger rotor with sensors to monitor its dynamic behavior under the influence of the aforementioned faulty condition was also developed. Following Modal Expansion, curve fitting technique was used to minimize the error between a set of equivalent experimental and numerical results. From the results, the theoretical Frequency response functions developed from Finite Element Method fault models had good agreement with the Time and frequency-based responses measured from experimental data for the induced imbalance fault condition, hence, validating the theoretical fault models developed in this study. Using Modal Expansion technique, data from nodal residual forces generated from the developed numerical fault model was compared with measured corresponding experimental nodal residual forces data. The results showed good agreement between the theoretical and experimental findings. Hence, the Model based fault identification scheme implemented in this study successfully identified the magnitude, severity and exact location of imbalance faulty conditions. Keywords: Model based, fault detection and identification, Turbocharger, vibration, Modal expansion, Test rig, Imbalance, modelling. DOI: 10.7176/ISDE/12-2-06 Publication date: August 30th 202

Identification of Rub and Unbalance in a 320MW Turbogenerators

The paper presents two experiences of application of a model based fault identification method on real machines. The first case presented is an unbalance identification on a 320MW turbogenerator unit operating in a fossil power plant. In the second case, concerning a machine of the same size but of a different manufacturer, the LP turbine was affected by a rub in the sealings. This time, the fault is modeled by local bows. The identification of the faults is performed by means of a model based identification technique in frequency domain, suitably modified in order to take into account simultaneous faults. The theoretical background of the applied method is briefly illustrated and some considerations are presented also about the best choice of the rotating speed set of the run-down transient to be used for an effective identification and about the appropriate weighting of vibration measurements at the machine bearings

A hierarchical Bayesian regression framework for enabling online reliability estimation and condition-based maintenance through accelerated testing

Thanks to the advances in the Internet of Things (IoT), Condition-based Maintenance (CBM) has progressively become one of the most renowned strategies to mitigate the risk arising from failures. Within any CBM framework, non-linear correlation among data and variability of condition monitoring data sources are among the main reasons that lead to a complex estimation of Reliability Indicators (RIs). Indeed, most classic approaches fail to fully consider these aspects. This work presents a novel methodology that employs Accelerated Life Testing (ALT) as multiple sources of data to define the impact of relevant PVs on RIs, and subsequently, plan maintenance actions through an online reliability estimation. For this purpose, a Generalized Linear Model (GLM) is exploited to model the relationship between PVs and an RI, while a Hierarchical Bayesian Regression (HBR) is implemented to estimate the parameters of the GLM. The HBR can deal with the aforementioned uncertainties, allowing to get a better explanation of the correlation of PVs. We considered a numerical example that exploits five distinct operating conditions for ALT as a case study. The developed methodology provides asset managers a solid tool to estimate online reliability and plan maintenance actions as soon as a given condition is reached.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Ship Design, Production and Operation

PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies

In recent years there has been significant progress in time series anomaly detection. However, after detecting an (perhaps tentative) anomaly, can we explain it? Such explanations would be useful to triage anomalies. For example, in an oil refinery, should we respond to an anomaly by dispatching a hydraulic engineer, or an intern to replace the battery on a sensor? There have been some parallel efforts to explain anomalies, however many proposed techniques produce explanations that are indirect, and often seem more complex than the anomaly they seek to explain. Our review of the literature/checklists/user-manuals used by frontline practitioners in various domains reveals an interesting near-universal commonality. Most practitioners discuss, explain and report anomalies in the following format: The anomaly would be like normal data A, if not for the corruption B. The reader will appreciate that is a type of counterfactual explanation. In this work we introduce a domain agnostic counterfactual explanation technique to produce explanations for time series anomalies. As we will show, our method can produce both visual and text-based explanations that are objectively correct, intuitive and in many circumstances, directly actionable.Comment: 9 Page Manuscript, 1 Page Supplementary (Supplement not published in conference proceedings.

Adaptable and Explainable Predictive Maintenance: Semi-Supervised Deep Learning for Anomaly Detection and Diagnosis in Press Machine Data

Predictive maintenance (PdM) has the potential to reduce industrial costs by anticipating failures and extending the work life of components. Nowadays, factories are monitoring their assets and most collected data belong to correct working conditions. Thereby, semi-supervised data-driven models are relevant to enable PdM application by learning from assets’ data. However, their main challenges for application in industry are achieving high accuracy on anomaly detection, diagnosis of novel failures, and adaptability to changing environmental and operational conditions (EOC). This article aims to tackle these challenges, experimenting with algorithms in press machine data of a production line. Initially, state-of-the-art and classic data-driven anomaly detection model performance is compared, including 2D autoencoder, null-space, principal component analysis (PCA), one-class support vector machines (OC-SVM), and extreme learning machine (ELM) algorithms. Then, diagnosis tools are developed supported on autoencoder’s latent space feature vector, including clustering and projection algorithms to cluster data of synthetic failure types semi-supervised. In addition, explainable artificial intelligence techniques have enabled to track the autoencoder’s loss with input data to detect anomalous signals. Finally, transfer learning is applied to adapt autoencoders to changing EOC data of the same process. The data-driven techniques used in this work can be adapted to address other industrial use cases, helping stakeholders gain trust and thus promote the adoption of data-driven PdM systems in smart factories

Data-driven extraction and analysis of repairable fault trees from time series data

Fault tree analysis is a probability-based technique for estimating the risk of an undesired top event, typically a system failure. Traditionally, building a fault tree requires involvement of knowledgeable experts from different fields, relevant for the system under study. Nowadays’ systems, however, integrate numerous Internet of Things (IoT) devices and are able to generate large amounts of data that can be utilized to extract fault trees that reflect the true fault-related behavior of the corresponding systems. This is especially relevant as systems typically change their behaviors during their lifetimes, rendering initial fault trees obsolete. For this reason, we are interested in extracting fault trees from data that is generated from systems during their lifetimes. We present DDFTAnb algorithm for learning fault trees of systems using time series data from observed faults, enhanced with Naïve Bayes classifiers for estimating the future fault-related behavior of the system for unobserved combinations of basic events, where the state of the top event is unknown. Our proposed algorithm extracts repairable fault trees from multinomial time series data, classifies the top event for the unseen combinations of basic events, and then uses proxel-based simulation to estimate the system’s reliability. We, furthermore, assess the sensitivity of our algorithm to different percentages of data availabilities. Results indicate DDFTAnb’s high performance for low levels of data availability, however, when there are sufficient or high amounts of data, there is no need for classifying the top event