Maintenance and inspections of ships performed by DNV GL have traditionally been based on a preventive scheme where components have been overhauled or maintained according to a time schedule. This philosophy is based on the assumption that a component has a defined lifetime, after which its failure rate increases. However, estimates of lifetime have large uncertainties and a large percentage of failures are not age-related, and are therefore not adequately addressed by preventive scheduled maintenance. DNV GL aim to develop radically new statistical approaches based on the recent availability of large arrays of sensors, which monitor condition and behaviour of machinery and power systems. Data are ubiquitous: almost every activity in which DNV GL engages produces and requires data. More and more ships get sensors installed, collecting more and more data. Data are the critical inputs into almost all decisions. Statistical inference is needed to turn data into knowledge, to understand unexplained mechanisms, discover hidden patterns, and predict future behaviours. As the accessibility, volume and complexity of data increases, new model based statistical and machine learning methods must be developed. Sensor data from ship generate very high dimensional time series, which relate to each other either in a known way (network of sensors) or in terms of stochastic dependence (correlation, coherence). These will be analysed for motif discovery, anomaly detection and classification. The purpose is to automatically alert about shifts in trends, variability or extremes, about changing patterns of behaviour or about any potential deviations from the norm. We will in particular consider state space models and other Bayesian hierarchical models. Both classification of faults, prediction of condition and faults, and optimization of performance are possible key aspects of this PhD.
Project leader: Erik Vanem
Institution: DNV AS