Machine Learning for Cyber Physical Systems

This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments

Diagnosis driven Anomaly Detection for CPS

In Cyber-Physical Systems (CPS) research, anomaly detection (detecting abnormal behavior) and diagnosis (identifying the underlying root cause) are often treated as distinct, isolated tasks. However, diagnosis algorithms require symptoms, i.e. temporally and spatially isolated anomalies, as input. Thus, anomaly detection and diagnosis must be developed together to provide a holistic solution for diagnosis in CPS. We therefore propose a method for utilizing deep learning-based anomaly detection to generate inputs for Consistency-Based Diagnosis (CBD). We evaluate our approach on a simulated and a real-world CPS dataset, where our model demonstrates strong performance relative to other state-of-the-art models

Autonomous Load Profile Recognition in Industrial DC-Link Using an Audio Search Algorithm

Industrial manufacturing plants, including machine tools, robots, and elevators, perform dynamic acceleration and braking processes. Recuperative braking results in an increased voltage in the machines' direct current (DC) links. In the case of a diode rectifier, a braking resistor turns the surplus of energy into lost heat. In contrast, active rectifiers can feed the braking energy back to the AC grid, though they are more expensive than diode rectifiers. DC link-coupled energy storage systems are one possible solution to downsize the supply infrastructure by peak shaving and to harvest braking energy. However, their control heavily depends on the applied load profiles that are not known in advance. Especially for retrofitted energy storage systems without connection to the machine control unit, load profile recognition imposes a major challenge. A self-tuning framework represents a suitable solution by covering system identification, proof of stability, control design, load profile recognition, and forecasting at the same time. This paper introduces autonomous load profile recognition in industrial DC links using an audio search algorithm. The method generates fingerprints for each measured load profile and saves them in a database. The control of the energy storage system then has to be adapted within a critical time range according to the identified load profile and constraints given by the energy storage system. Three different load profiles in four case studies validate the methodology

Approach to a Decision Support Method for Feature Engineering of a Classification of Hydraulic Directional Control Valve Tests

Advancing digitalization and high computing power are drivers for the progressive use of machine learning (ML) methods on manufacturing data. Using ML for predictive quality control of product characteristics contributes to preventing defects and streamlining future manufacturing processes. Challenging decisions must be made before implementing ML applications. Production environments are dynamic systems whose boundary conditions change continuously. Accordingly, it requires extensive feature engineering of the volatile database to guarantee high generalizability of the prediction model. Thus, all following sections of the ML pipeline can be optimized based on a cleaned database. Various ML methods such gradient boosting methods have achieved promising results in industrial hydraulic use cases so far. For every prediction model task, there is the challenge of making the right choice of which method is most appropriate and which hyperparameters achieve the best predictions. The goal of this work is to develop a method for selecting the best feature engineering methods and hyperparameter combination of a predictive model for a dataset with temporal variability that treats both as equivalent parameters and optimizes them simultaneously. The optimization is done via a workflow including a random search. By applying this method, a structured procedure for achieving significant leaps in performance metrics in the prediction of hydraulic test steps of directional valves is achieved

Distributed Analytics Framework for Integrating Brownfield Systems to Establish Intelligent Manufacturing Architecture

Intelligent manufacturing otherwise called as smart manufacturing concentrates upon optimising production and processes by making full use of data available. It is regarded as a new manufacturing model where the entire product life cycle can be simplified using various smart sensors, data-driven decision-making models, visualisation, intelligent devices, and data analytics. In the Industry 4.0 era, Industrial Internet of Things (IIoT) architecture platform is required to streamline and secure data transfer between machines, factories, etc. When certain manufacturing industry is equipped with this platform, an intelligent manufacturing model can be achieved. In today’s factories, most machines are brownfield systems and are not connected to any IoT platforms. Thus they cannot provide data or visibility into their performance, health, and optimal maintenance schedules, which would have improved their operational value. This paper attempts to bridge this gap by demonstrating how brownfield equipment can be IIoT enabled and how data analytics can be performed at the edge as well as cloud using two simple use cases involving industrial robot on the abrasive finishing process. The focus of this paper is on how a scalable data analytics architecture can be built for brownfield machines at the edge as well as the cloud

ANOMALY DETECTION - REVIEW OF METHODS, TOOLS AND ALGORITHMS

This paper contains review of algorithms, methods and tools nowadays used for anomaly detection.Anomaly detection is used in many domains of science and industry, some authors classify anomaly detection as data mining and data science tool, others state it is decision support tool under artificial intelligence domain and indeed the use cases of anomaly detection are very different.The article describes the main algorithms used for anomaly detection from perspective of theory of computer science and practical use cases of anomaly detection in different domains of industry. Several paragraphs are dedicated to the frameworks used by one of the most popular and powerful anomaly detection tools available in the market - Microsoft Anomaly detector.