In the presented work, the detection of anomalous energy consumption in hybrid industrial production systems is investigated. A model-based approach with a timed hybrid automaton as overall system model is employed for anomaly detection. The approach is based on the assumption of several system modes, i.e. phases with continuous system behavior. Transitions between the modes are attributed to discrete control events such as on/off signals. The underlying discrete event system which comprises both system modes and transitions is modeled as finite state machine. The focus of this paper is set on the modeling of the energy consumption in the particular system modes. Sequences of stochastic state space models are employed for this purpose. Model learning and anomaly detection for this approach are considered. The proposed approach is further evaluated in a small model factory. The experimental results show significant improvements compared to existing approaches to anomaly detection in hybrid industrial systems

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In this paper, fault detection in piecewise stationary industrial processes is investigated. Such processes can be modeled as sequences of distinct system modes in which the respective expectation values and variances of process variables do not change. In particular, piecewise stationary processes with autonomous transitions between system modes are considered in this work, i.e. processes without observable trigger events such as on/off signals. A Hidden Markov Model (HMM) is employed as underlying system model for such processes. System modes are modeled as hidden state variables with given transition probabilities. Continuous process variables are assumed to be Gaussian distributed with constant second order statistics in each system mode. A novel HMM-based fault detection method is proposed which incorporates the Viterbi algorithm into a fault detection method for hybrid industrial processes. Experimental results for the proposed fault detection method are presented for a module of the Lemgo Smart Factory

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In the present work, fault detection in industrial automation processes is investigated. A fault detection method for observable process variables is extended for application cases, where the observations of process variables are noisy. The principle of this method consists in building a probability distribution model and evaluating the likelihood of observations under that model. The probability distribution model is based on a hybrid automaton which takes into account several system modes, i.e. phases with continuous system behaviour. Transitions between the modes are attributed to discrete control events such as on/off signals. The discrete event system composed of system modes and transitions is modeled as finite state machine. Continuous process behaviour in the particular system modes is modeled with stochastic state space models, which incorporate neural networks. Fault detection is accomplished by evaluation of the underlying probability distribution model with a particle filter. In doing so both the hybrid system model and a linear observation model for noisy observations are taken into account. Experimental results show superior fault detection performance compared to the baseline method for observable process variables. The runtime of the proposed fault detection method has been significantly reduced by parallel implementation on a GPU

In this paper, stochastic models for fault detection in industrial automation processes are investigated. Thereby, nonlinear, time-variant systems are considered. The basic idea consists in building a probability distribution model and evaluating the likelihood of observations under that model. In contrast to the existing methods, this paper considers the practically important case in which measurement noise is negligible and all process variables are observable. This assumption allows the direct evaluation of a probability distribution for fault detection without approximations such as second order statistics or particles. The main part of this paper deals with adequate models for this probability distribution such as Gaussian and Hidden Markov models. Such models require predictions of the expectation values of the respective probability distributions. Regression models such as (multivariate) linear regression models and neural networks are investigated for this purpose. Evaluations are conducted with respect to prediction accuracies and fault detection capabilities of the employed models. Evaluations show superior results of the novel approach compared to existing fault detection methods, which are based on approximations such as second order statistics

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This paper addresses the optimization of energy flows between electric drives in conveying systems. Thereby, load peaks and the feedback of electric energy into the grid are reduced. The approach is based on the solution of a mixed integer quadratic optimization problem which incorporates models for energy flows and energy consumption of the electric drives. Models for energy flows and energy consumption can be parametrized from sensor data. Besides, movement constraints such as start positions, end positions and time limits are taken into account. The solution of the optimization problem is accomplished in realtime by application of standard solvers. Experimental results show that the proposed methods allows recovering regenerative energy of electric drives as motoric power for other drives. Integration into material flow planning of an automated warehouse is straightforward so that an inexpensive and simply usuable way for power saving in intralogistics is presented