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An important part of the industry 4.0 concept is the horizontal and vertical integration of manufacturing systems.
Information exchange in traditional production environments happens through interfaces that are connections between strictly defined senders and receivers. This limits the possibility for changing and extending the manufacturing system. A possible approach to enable the information exchange between all system entities uniformly are information models. Such models are semantic descriptions of the available data. The creation of these models needs to follow the manufacturing process, but also requires certain standardization to improve efficiency. Another challenge is the actual technical integration of the information into a common address space.
This paper connects an approach for information modeling with a concept for dynamic aggregation. The approach is described with the help of a continuous example that uses OPC UA as a middleware technology.
Acoustic emission (AE) signals obtained during machining processes can be used to detect, locate and assess flaws in structures made of metal, concrete or composites. This paper aims to characterize AE signals using derived parameters from raw signatures along with statistical feature extractions to correlate with tool wear readings. Missing tool wear values are imputed using domain knowledge rules and compared to AE signals using machine learning models. The amount of effect on tool wear is formulated using Bayesian Inferences on derived parameters such as areas under the raw signal curve in addition to comparisons with the supervised models for predictions. Using the constructed models and formulation, the presented study also includes a trace-back pseudo-algorithm for determining the stage in process where tool wear values begin to approach the wear limits.
In order to be able to serve constantly new customer requirements, manufacturing systems must be able to adapt to frequent changes. In addition, repeatedly objects are removed or added to the network. To control and monitor such a constantly changing system a mapping of existing manufacturing systems into a common information model is necessary. This model describes information that is produced and stored in different entities of the complete system. To create a common address space and expose the relations between the devices an aggregation of every element in the system is needed. This paper describes a methodology for the creation of an information model for a complete manufacturing environment, followed by an approach for the aggregation of the singular system entities. The concept of this paper is illustrated with a demonstrator. The results of this approach have been discussed in the following sections along with the proposal for further directions.
Database management and monitoring is an inseparable part of any industry. A uniform scheme of monitoring relational databases without explicit user access to database servers is not much explored outside the database environment. In this paper, we present an information distribution scheme related to databases using Open Platform Communication Unified Architecture (OPC UA) servers to clients when multiple databases are involved in a factory. The aim is for external, but relevant clients, to be able to monitor this information mesh independent of explicit access to user schemas. A methodology to dispense data from, as well as check changes in databases using SQL queries and events is outlined and implemented using OPC UA servers. The structure can be used as a remote viewing application for multiple databases in one address space of an OPC UA server.
Vibration data consists of batches of time series which if accumulated over a period of time is a huge collection of numeric data. Reducing such data for use in deep learning models for computational effciency is a challenge. Combinatorial and discrete approaches, on the other hand, is not an extensively explored area when it comes to datasets. This paper aims to identify feature reduction techniques based on discrete approaches such as euclidean distance using dot products on vibration data samples from accelerometers fitted on bearings. In this limited experimentation, the procured dataset by this approach is considerably smaller in size as compared to the actual complete data, and with comparable results in prediction models, it can be used as a smaller representation of a sensor timeline. The results based on different models show that such reductions can be considered in building IoT applications in industries based on sensors.
Open Platform Communications - Unified Architecture (OPC UA) as a communication protocol has been one of the front running IoT enablers in recent years. Many use cases in manufacturing domains demonstrate the vast inter-operability and cross-platform connectivity strengths of OPC UA. However, to consolidate its position in the IoT world, OPC UA needs to be able to bridge certain gaps, like interconnections across multiple database engines over different networks. This paper aims to investigate aggregation dynamics of OPC UA consisting of multiple database servers from a specific environment such as manufacturing. Prevailing databases over restricted networks such as production floors cannot be accessed by respective clients outside the system. In these cases, OPC UA services can provide an a selective but imperative view of data from these databases to the clients. The investigations in this paper are demonstrated with two different implementations of databases related to manufacturing with OPC UA. These applications show that using OPC UA as a means of connection and distribution of information from databases can be an effective solution to a common data view of all connections not necessarily of factory floor network.