Jonas Vlasselaer

LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines

Abstract Accurate prediction of forthcoming faults in modern industrial machines plays a key role in reducing production arrest, increasing the safety of plant operations, and optimizing manufacturing costs. The most effective condition monitoring techniques are based on the analysis of historical process data. In this paper we show how Least Squares Support Vector Machines (LS-SVMs) can be used effectively for early fault detection in an online fashion. Although LS-SVMs are existing artificial intelligence methods, in this paper the novelty is represented by their successful application to a complex industrial use case, where other approaches are commonly used in practice. In particular, in the first part we present an unsupervised approach that uses Kernel Spectral Clustering (KSC) on the sensor data coming from a vertical form seal and fill (VFFS) machine, in order to distinguish between normal operating condition and abnormal situations. Basically, we describe how KSC is able to detect in advance the need of maintenance actions in the analysed machine, due the degradation of the sealing jaws. In the second part we illustrate a nonlinear auto-regressive (NAR) model, thus a supervised learning technique, in the LS-SVM framework. We show that we succeed in modelling appropriately the degradation process affecting the machine, and we are capable to accurately predict the evolution of dirt accumulation in the sealing jaws.

Exploiting local and repeated structure in Dynamic Bayesian Networks

We introduce the structural interface algorithm for exact probabilistic inference in Dynamic Bayesian Networks. It unifies state-of-the-art techniques for inference in static and dynamic networks, by combining principles of knowledge compilation with the interface algorithm. The resulting algorithm not only exploits the repeated structure in the network, but also the local structure, including determinism, parameter equality and context-specific independence. Empirically, we show that the structural interface algorithm speeds up inference in the presence of local structure, and scales to larger and more complex networks.

ProbLog2: Probabilistic Logic Programming

We present ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog. The ProbLog language allows the user to intuitively build programs that do not only encode complex interactions between a large sets of heterogenous components but also the inherent uncertainties that are present in real-life situations. The system provides efficient algorithms for querying such models as well as for learning their parameters from data. It is available as an online tool on the web and for download. The offline version offers both command line access to inference and learning and a Python library for building statistical relational learning applications from the systems components.

T P -Compilation for inference in probabilistic logic programs

We propose T P -compilation, a new inference technique for probabilistic logic programs that is based on forward reasoning. T P -compilation proceeds incrementally in that it interleaves the knowledge compilation step for weighted model counting with forward reasoning on the logic program. This leads to a novel anytime algorithm that provides hard bounds on the inferred probabilities. The main difference with existing inference techniques for probabilistic logic programs is that these are a sequence of isolated transformations. Typically, these transformations include conversion of the ground program into an equivalent propositional formula and compilation of this formula into a more tractable target representation for weighted model counting. An empirical evaluation shows that T P -compilation effectively handles larger instances of complex or cyclic real-world problems than current sequential approaches, both for exact and anytime approximate inference. Furthermore, we show that T P -compilation is conducive to inference in dynamic domains as it supports efficient updates to the compiled model. A new inference technique for probabilistic logic programs.We interleave knowledge compilation with forward reasoning.Exact as well as anytime approximate inference.Our approach is conducive to inference in dynamic models.Empirical evaluation on various domains.

The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions

Probabilistic logic languages, such as ProbLog and CP-logic, are probabilistic generalizations of logic programming that allow one to model probability distributions over complex, structured domains. Their key probabilistic constructs are probabilistic facts and annotated disjunctions to represent binary and mutli-valued random variables, respectively. ProbLog allows the use of annotated disjunctions by translating them into probabilistic facts and rules. This encoding is tailored towards the task of computing the marginal probability of a query given evidence MARG, but is not correct for the task of finding the most probable explanation MPE with important applications e.g., diagnostics and scheduling. In this work, we propose a new encoding of annotated disjunctions which allows correct MARG and MPE. We explore from both theoretical and experimental perspective the trade-off between the encoding suitable only for MARG inference and the newly proposed general approach.

Condition monitoring with incomplete observations

We introduce an approach for predicting the behaviour of a machine during a production cycle. Typical data analysis methods assume that continuous behaviour is (fully) observed. This assumption is unrealistic as monitored machines are often interrupted and restarted at irregular points in time. We study the resulting problem, propose a solution and report on a use-case in wire drawing.