Aim:

        The aim of creating a modular ice cream factory dataset on anomalies in sensors to support machine learning research in manufacturing systems is to provide a valuable resource for the development and evaluation of machine learning models and anomaly detection algorithms in the context of manufacturing.

Abstract:

          A small deviation in manufacturing systems can cause huge economic losses, and all components and sensors in the system must be continuously monitored to provide an immediate response. The usual industrial practice is rather simplistic based on brute force checking of limited set of parameters often with pessimistic pre-defined bounds. The usage of appropriate machine learning techniques can be very valuable in this context to narrow down the set of parameters to monitor, define more refined bounds, and forecast impending issues. One of the factors hampering progress in this field is the lack of datasets that can realistically mimic the behaviors of manufacturing systems. In this paper, we propose a new dataset called MIDAS (Modular Ice cream factory Dataset on Anomalies in Sensors) to support machine learning research in analog sensor data. MIDAS is created using a modular manufacturing simulation environment that simulates the ice cream-making process. Using MIDAS, we evaluated three different supervised machine learning algorithms (Logistic Regression, Decision Tree and Random Forest) for two different problems: anomaly detection and anomaly classification. The results showed that Decision Tree is the most suitable algorithm with respect to model accuracy and execution time. We collect the Modular Ice Cream Factory Dataset from the publicly available, to enable interested researchers to enhance the state of the art by conducting further studies

Introduction:

            In a world where most of the commodities are mass produced by companies using automated manufacturing systems, the quality of those systems is of vital importance. Even a small deviation in parts of the system could potentially result in bad or malfunctioning products leading to customer dissatisfaction, environmental impacts or huge economic losses to the industry. This is the main reason why all components and sensors in the system have to be continuously monitored to identify anomalies, and prompt remedial actions should be provided if something goes wrong.

In a generic sense, an ‘anomaly’ is a deviation from expected behavior, and can occur for different reasons, including faults in the system or its configuration, or due to unanticipated external interference. Such interference, or even some system or configuration faults, belong to the realm of cyber security threats if the root cause is an act of bad intention. Regardless of the cause, the consequences of anomalies must be kept at acceptable levels

Proposed System:

           This research aims to create an effective prediction model using different types of ML methods to detect anomalies in Ice cream factory. First of all, the datasets are collected, and then the preprocessing is accomplished via the missing values imputation and Create an instance of RFE with the classifier and the desired number of features to select Logistic Regression classification of modeling and performance evaluation. Then we are using Decision Tree, Logistic regression and Random Forest Algorithm for prediction accuracy. Decision Tree gives a best results with respect to high accuracy. Compare to existing system our new system gives results are more Accuracy.