Early-Stage Researcher position in Machine Learning for Sensor Data Validation
Siemens Digital Industries Software is seeking an early-stage researcher (ESR) to join the European Training Network on Monitoring Large-Scale Complex Systems (“MOIRA”), funded by the European Commission. The ESR will research machine learning based methods for automatic detection of incorrect sensor data. The position offers a generous salary and opportunity for short research visits to other organizations. Apply by 28 May 2023 at https://euraxess.ec.europa.eu/jobs/94075.
PhD position at KU Leuven
Unsupervised self-learning method based on streaming fleet data.
ESR2 will work on machine-learning and artificial-intelligence methods for self-learning and self-monitoring cyber-physical systems. A framework based on novelty detection will be established using dynamically updated clustering methodologies, capturing the transition of the system between multiple steady and time-varying operating modes. Moreover, self-organizing model methods will be investigated and further developed.
PhD position at INSA
Probabilistic fleet monitoring, combination of dynamic and probabilistic models, information geometry on a manifold of models.
ESR3 will develop model-based techniques for monitoring individual units in a fleet from a probabilistic approach. The principle is based on the comparison of a parametric model identified from measured data with a generic model provided by dynamic modelling, by taking into account the acceptable differences between the generic model of the fleet and individual units. This will be solved as an inverse problem in the presence of “modelling errors” that reflect the departure of individual units from the nominal model representing the fleet. After identification of the model parameters from experimental data – a non-trivial issue due to the fact that modelling errors are highly structured and do not comply with the simplifying assumption commonly advocated in system-identification theory – each unit will be assigned a different realization of the same model, seen as a random object.
PhD position at INSA
Novel approach in inverse modelling and model selection for fleet monitoring.
ESR4 will work on fleet monitoring, using a generic model and solving an inverse identification problem. The choice and the use of different models that map the physical quantities of interest to the measured signals will be investigated in order to improve the general performances of a “virtual sensor”, which is achieved through inverse modelling. The richness of the sensor equipment of a unit (machine, vehicle etc.) will be used in order to correlate the virtual signal estimations from different inverse model architectures. Methodologies will be proposed for the identification of the particular operating conditions of each unit, updating simultaneously their estimation by sharing reconstructed signals of the fleet. The methodologies will be applied to fleets of vehicles and wind turbines.
PhD position at UOXF
Deep learning, information fusion, probabilistic assessments.
ESR5 will develop methods based on recent advances in deep learning to fuse the time-series data of patients’ vital signs along with data from the electronic health records to provide probabilistic assessments of patient health. This research will build on proof-of-principle studies in which we have developed methods based on Gaussian processes (GPs), which are a Bayesian nonparametric means of performing inference by defining probability distributions over (potentially infinite-dimensional) objects such as time-series functions.
PhD position at CU
Development of hybrid prognostic technologies.
ESR6 will develop a novel hybrid prognostic methodology focusing on the assessment of the state of the health of a system. The methodology will integrate physics-based and data-driven prognostics models in order to enhance the prognostic accuracy, robustness and applicability. Dedicated experimental test rigs such as a clogged filter, a machinery fault simulator and a linear actuator failure simulator will be used to obtain reproducible datasets under different operating conditions. Finally, the performance of the developed technique will be evaluated based on the most recent prognostic evaluation metrics.
PhD position at LTU
Strategy to digitalize human intuitive cognition including psychological, philosophical, and engineering stances.
ESR7 will focus on the identification of the elements that are necessary for machines to conduct maintenance actions. More specifically, implicit situation awareness measurement techniques and state-of-the-art techniques will be used in the exploration of cognitive tasks as a platform to develop extended methods and to explore intuitive cognition. An empirical study to explore the intuitive cognition phenomena is necessary; to explore and answer the what and why questions. Such studies can also contribute to refining the theoretical foundation as well as to improving the human cognition taxonomy at different levels of maintenance automation. This is a pertinent issue, to obtain the acquisition and transfer of tacit maintenance know how, from legacy systems to intelligent systems. There is an untapped potential of simulation for digital training using virtual reality solutions, not only for humans to gain skills but also for intelligent systems to learn from intuitive human interactions.
PhD position at UNIMORE
The development of a hybrid condition monitoring system combining model-based and data-driven methodologies.
ESR8 will work on the condition monitoring of motioncontrol applications, such as independent cart conveyors. These conveyor systems are an emerging technology in industries seeking to replace induction motors and kinematic chains in several applications. The condition monitoring of these systems is a challenging industrial activity that will be developed in three steps. Firstly, a data-driven approach will be considered to perform anomaly detection on the main parts of motion-control applications and ESR8 will work on clustering algorithms, data-reduction and classification. Afterwards, a model-based approach using a few subsets of critical components will be developed, ensuring accuracy in the diagnosis and optimization of computational resources.
PhD position at WUST
Development of novel modelling/analysis/separation approaches for long-term, big Data, heterogeneous processes for condition monitoring with a focus on non-stationary operation and heavy-tailed, non-Gaussian noise.
ESR9 will focus on the analysis of long-term, heterogeneous, historical big-data from time-varying systems operating under impulsive, non-Gaussian noise. Mining machines are very specific: unique, complex and designed for heavy duty, they operate under non-stationary conditions, they can experience multiple faults and they are frequently overloaded (especially during start-up or emergency stops). The condition monitoring of these machines is complicated. Often, diagnostics measurements are affected by the environmental conditions (temperature) and impulsive disturbances (impulsive noise). The monitoring of such machines requires the processing of a huge number of different variables and the nearly online validation and processing of data.
PhD position at FhG
Simultaneous monitoring of infrastructure and sensors network degradation.
ESR10 will develop a methodology, based on the Failure-Modes-and-Effects-Analysis (FMEA) and multi-class classification methods, to monitor large-scale infrastructures, such as bridges and wind turbines, as well as the sensor network that is used. In special use-cases in rough environments, e.g., if the submerged structures of offshore wind turbines should be monitored, it is commonly accepted that the sensors will only have a useful lifetime of less than one year (so called ‘lost sensors’). A novel method will be developed to replace the information coming from the lost sensors by information from the remaining sensors in the network, replacing the “lost sensors” by “virtual” ones. The methodologies will be validated using real data from a full-scale wind turbine (2 MW, 100 m hub-height) equipped with a network of smart sensors.
PhD position at IKER
Development of reliable stress estimation algorithms, application to fatigue life prediction, validation in test rigs of industrially relevant systems.
ESR11 will define a methodology to evaluate the remaining fatigue life of large uncertain industrial systems. The methodology will focus on obtaining reliable stress time series at fatigue-critical locations of industrial systems under operational conditions. The estimated stresses will then be used with advanced fatigue-and-damage tolerance techniques in order to assess the remaining useful life of the system. In this way the evaluation of the current fatigue life will shift from a “worst-case scenario” to a more realistic “current-case scenario”. Therefore, with this methodology, the evaluation of the remaining useful life of large industrial machinery will be improved, allowing a shift from predictive to condition-based maintenance.
PhD position at SISW
Transfer learning applied to simulation data and fleet data for time series prediction.
ESR12 will focus on novel Predictive Engineering Analytics (PEA) methodologies for predicting the performance and usage of assets in a fleet throughout the product lifecycle. In particular, machine learning methods allowing for
the prediction of time series data are of interest. When applied to the prediction of sensor data (i.e., so-called “virtual sensing”), this allows monitoring physical quantities (e.g. vibration, sound, strain, …) in locations which are infeasible to be measured directly with a sensor.
PhD position at SISW
Machine learning methods for automatic validation of multiple sensors, whereby in particular semi-supervised learning and multi-sensor strategies beyond the current state-of-the-art.
ESR13 will investigate machine-learning methods that are trained to recognize incorrect sensor data. A systematic approach will be followed: in the first stage, a supervised learning approach will be adopted, whereby it is assumed that an historical dataset with fully labelled examples is available. As this assumption might not prove to be practically realizable in many cases, an unsupervised anomaly-detection approach will be investigated in the second stage. Such an approach does not require labelled data, but is typically more difficult to implement successfully compared to a supervised approach.
PhD position at AMC VIBRO
Development of data-fusion techniques for the exploitation of heterogeneous data captured by robotic devices for monitoring of mines.
ESR14 will develop methodologies to fuse and exploit information extracted from heterogeneous data, including vibration, temperature, images, sound and local environmental parameters, captured by inspection robots, for monitoring and diagnosing machines and infrastructure (electric cables, pipelines, structures) used in deep mines. Due to the harsh environment and natural hazards (gas and rock outbursts, seismic risks), the future of mining is related to introducing fully automated machinery and robotic inspection systems in order to minimize the risks for humans. The development of mining-inspection robots equipped with various types of sensors, including microphones, accelerometers, temperature/humidity/gas sensors and cameras, will provide a massive and heterogeneous amount of data gathered under harsh mining conditions.
PhD position at SAFRAN
Condition monitoring of aircraft engines using smartphones/tablets.
ESR15 will work on the analysis, identification and modelling of vibratory and acoustic sources of aircraft engines. Vibratory and acoustic sources include random phenomena related to fuel combustion, cyclic phenomena related to the rotations of shafts/rotors and nonlinear phenomena related to the fluidic link between the high-pressure and the low-pressure turbines. The digital industrial evolution is characterized by an interconnection of machines and systems (objects) that is mainly based on the use of many sensors that collect information, but with a degraded quality related to the nature of the “general public” sensors they are equipped with.