Thesis Description
Machine shops usually rely on Computer Numerical Control (CNC) controlled Machine Tools (MTs). The impact of downtime of a machine is seen as the biggest concern for the operation of these MTs, followed by machine stop issues [1]. In [1] Adu-Amankwa et al. estimate that around 55.000e could be saved per machine per year using Predictive Maintenance (PdM) techniques.
In the modern industry, PdM is used to improve the decision-making process for the maintenance activity and consequently strives to reduce downtime [2]. Contrary to established strategies, maintenance is performed based on the estimated health of the equipment rather than a planned schedule or after failure [3]. As predictive maintenance usually requires a lot of data, Machine Learning (ML) techniques are commonly used in this field [2].
Two of the most common types of ML are supervised and unsupervised learning. Unsupervised approaches don’t need labeled data, while this is a requirement for supervised learning [4]. The data provided by the machines is unlabeled and only a few anomalies are contained in the dataset. Therefore for this thesis, an unsupervised approach is taken. One of the possible applications for unsupervised learning is the field of Anomaly Detection, which will be utilized in this thesis [4].
Anomaly Detection is the process of finding patterns in the presented data, that lie outside of the expectations. It finds its application in fraud detection for credit cards, insurance or healthcare, military surveillance, and fault detection of critical systems [5]. The manufacturing industry faces the challenge that only a small percentage of anomalies can be detected beforehand. A rotating piece of equipment will deteriorate over time which leads to abnormal behavior which should be considered as a caution of the current state of the equipment. As manufacturing sensor data is generally time-based and are collected over longer periods it makes it difficult to use in typical ML techniques. Therefore techniques that can be used with input sequences of variable length are required [6].
Neural Networks using recurrent Autoencoders which try to reconstruct the input data is one of the state of-the-art methods for time series Anomaly Detection [7]. Other methods include the Local Outlier Factor algorithm for the calculation of the distance metric between time series [8] and the utilization of a Generative Adversarial Network (GAN) including LSTMs (Long Short Term Memory) to capture temporal correlation [9]. Another method of anomaly detection is the analysis of the spectrogram generated by Short-Time Fourier Transforms of the time series [10].
Autoencoders learn only the most significant features, due to the compression to a compact hidden representation, are commonly used. Anomalies are usually missing representative features and, therefore Autoencoders fail to reconstruct the input [11]. This approach relies on the fact that the training data does not contain any anomalies, as anomalies being present in the training data can have a negative effect on the hidden representation and result in bad performance for this kind of data [7].
In this thesis, the unlabeled error data of the CNC MTs will be analyzed and different techniques for Anomaly Detection will be tested. Another part of the thesis will to identify the likely component that caused the error.
In summary, the thesis deals with the following topics:
- Data analysis
(a) Pre-Processing
(b) Manual identification of anomalies - Development of various Deep Learning Models for Anomaly Detection
- Evaluation of the Deep Learning Models regarding various metrics including
(a) Accuracy
(b) Training performance
(c) Runtime performance - Suggest component for Anomaly data
References
[1] Kwaku Adu-Amankwa, Ashraf K.A. Attia, Mukund Nilakantan Janardhanan, and Imran Patel. A predictive maintenance cost model for cnc smes in the era of industry 4.0. The International Journal of Advanced Manufacturing Technology, 104(9):3567–3587, Oct 2019.
[2] Tiago Zonta, Cristiano Andr´e da Costa, Rodrigo da Rosa Righi, Miromar Jos´e de Lima, Eduardo Silveira da Trindade, and Guann Pyng Li. Predictive maintenance in the industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150:106889, 2020.
[3] Gian Antonio Susto, Alessandro Beghi, and Cristina De Luca. A predictive maintenance system for epitaxy processes based on filtering and prediction techniques. IEEE Transactions on Semiconductor Manufacturing, 25(4):638–649, 2012.
[4] Ritu Sharma, Kavya Sharma, and Apurva Khanna. Study of supervised learning and unsupervised learning. International Journal for Research in Applied Science and Engineering Technology, 8(6):588–593, 2020.
[5] Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM Comput. Surv., 41, 07 2009.
[6] Kamat, Pooja and Sugandhi, Rekha. Anomaly detection for predictive maintenance in industry 4.0- a survey. E3S Web Conf., 170:02007, 2020.
[7] Tung Kieu, Bin Yang, Chenjuan Guo, Razvan-Gabriel Cirstea, Yan Zhao, Yale Song, and Christian S. Jensen. Anomaly detection in time series with robust variational quasi-recurrent autoencoders. In 2022 IEEE 38th International Conference on Data Engineering (ICDE), pages 1342–1354, 2022.
[8] Wang Yong, Mao Guiyun, Chen Xu, and Wei Zhengying. Anomaly detection of semiconductor processing data based on dtw-lof algorithm. In 2022 China Semiconductor Technology International Conference (CSTIC), pages 1–3, 2022.
[9] Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. Tadgan: Time series anomaly detection using generative adversarial networks. In 2020 IEEE International Conference on Big Data (Big Data), pages 33–43, 2020.
[10] Hongzu Li and Pierre Boulanger. Structural anomalies detection from electrocardiogram (ecg) with spectrogram and handcrafted features. Sensors, 22(7), 2022.
[11] Ane Bl´azquez-Garc´ıa, Angel Conde, Usue Mori, and Jose A. Lozano. A review on outlier/anomaly detection in time series data. ACM Comput. Surv., 54(3), apr 2021.