Thesis Description
Maintaining machines causes large expenses in the modern industrial world. For example, in the product manufactoring sector, maintenance makes up to 15%-60% of their costs [1]. To reduce these costs, more efficient methods of maintenance were invented. In general, Maintenance can be divided into three different categories [2]:
- Corrective: The machine’s failure has already occured.
- Preventive: The maintanence is done on a regular basis to decrease the likelihood of a failure.
- Predictive: Keep track of the machine’s condition and prediction of its failure occurance.
Predictive Maintenance uses industrial data modeling and analysis to perform equipment fault diagnosis through real-time monitoring. By direct application of domain knowledge onto given problems, it is possible to predict that status and its trend with respect to possible failures. With this, maintenance plans become more accurate. This reduces unplanned downtime as well as cost of operation [1]. One common problem in the field of predictive maintenance is the detection of anomalies. As the name implies, the classifier’s task is to detect anomalies within data by comparing it to expected data. These anomalies are used as indicators for problems and basis for the failure prediction [2]. Anomalies can be found in any kind of data. In this thesis the focus is on machines with a SINAMICS Low
Voltage Converter [3]. In general, a converter adapts the constant AC to AC with various frequency and voltage [4]. The SINAMICS Low Voltage Converter produces sensor data in the high frequency spectrum based on a low voltage [3]. The data is gathered and computed on an Industrial Edge [5]. Edge Computing is defined as any computing done at the edge of the network that produced the data. Compared to Cloud Computing which bottleneck is the massive data transfer, Edge Computing saves a lot of bandwith since the data is already processed and only the relevant information is sent to the cloud [6].
Anomaly detection is already used in different security aspects like fraud, intrusion detection but also regarding medical health anomalies. As a result, there exist various methods for different kind of data (continous, discret, labeled or unlabeled etc.). Since in this thesis not all anomalies are known, the given data is unlabeled and there is a time element, the focus in this thesis will be on methods for unsupervised time series data, for example, Hidden Markov Models, Clustering, Neural Network and Nearest neighbour, etc. [7, 8, 9]. The goal of this thesis is to apply different methods for the detection and classification of signal anomalies resulting in a comparison between them regarding resource usage (e.g. RAM), runtime and accuracy for a specific type of converter. To be able to accurately predict any failures at the machine, the anomalies will be ranked regarding their severness in respect to the machine. This thesis aims to find a robust, exible and unsupervised model for anomaly detection in high frequency, low voltage sensor data as a basis for Predictive Maintenance. The data is computed directly on an Industrial Edge device (227E, 427E) attached to the SINAMICS Low Voltage Converter. In summary, the thesis deals with the following points:
- Define anomalies and their consequences/meanings for the SINAMICS Low Voltage Converter:
(a) Detection and identification of anomalies
(b) Ranking of found anomalies based on their inuence - Development of different models for anomaly detection and classification
- Evaluation based on the different models considering the following points:
(a) Accuracy
(b) Flexibility
(c) Resource Usage
(d) Robustness
(e) Runtime
References
[1] Bei-ke Zhang Cheng Cheng and Dong Gao. A predictive maintenance solution for bearing production line
based on edge-cloud cooperation. In 2019 Chinese Automation Congress (CAC), pages 5885{5889, 2019.
[2] Masaru Kurihara Pushe Zhao, Shigeyoshi Chikuma Junichi Tanaka, Tojiro Noda, and Tadashi Suzuki. Advanced
correlation-based anomaly detection method for predictive maintenance. In 2017 IEEE International
Conference on Prognostics and Health Management (ICPHM), pages 78{83. IEEE, 2017.
[3] Siemens Global Website. Siemens Digital Industries. https://new.siemens.com/global/en/products/
drives/sinamics/low-voltage-converter.html, 1996-2021. [Online; accessed 23-02-2021].
[4] Pawel Szczesniak Zbigniew Fedyczak and Marius Klytta. Ac drives with buck-boost voltage switched frequency
converters without dc storage. In 2018 International Symposium on Power Electronics, Electrical
Drives, Automation and Motion (SPEEDAM), pages 507{512, 2018.
[5] Siemens Global Website. Industrial Edge. https://new.siemens.com/global/en/products/automation/
topic-areas/industrial-edge/simatic-edge.html, 1996-2021. [Online; accessed 18-02-2021].
[6] Jie Cao Weisong Shi, Youhuizi Li Quan Zhang, and Lanyu Xu. Edge computing: Vision and challenges.
IEEE Internet of Things Journal, 3(5):637{646, 2016.
[7] Charu C. Aggarwal Manish Gupta, Jing Gao and Jiawei Han. Outlier detection for temporal data: A survey.
IEEE Transactions on Knowledge and Data Engineering, 26(9):2250{2267, 2014.
[8] Lei Clifton Marco A.F. Pimentel, David A. Clifton and Lionel Tarassenko. A review of novelty detection.
Signal Processing, 99:215{249, 2014.
[9] Arindam Banerjee Varun Chandola and Vipin Kumar. Anomaly detection: A survey. ACM Computing
Surveys, 41:1{58, 2009.