Unsupervised Contextual Anomaly Detection in Frequency Converter Data

Type: MA thesis

Status: finished

Date: January 1, 2023 - July 3, 2023

Supervisors: Aleksandra Thamm, Florian Thamm, Benjamin Amschler (SIEMENS AG), Ali Al Hage Ali (SIEMENS AG), Andreas Maier

Thesis Description

Anomaly detection is a widely researched topic and is already extensively used in many different domains such as fraud detection for credit cards, intrusion detection for cyber-security or fault detection in safety critical systems [1]. Various anomaly detection techniques are also applied in the industry where electric motors are one of the most important components which is underlined by the fact that they account for approximately 40% of global electricity consumption. Being able to detect anomalies in their sensor data can help predicting errors and therefore avoiding expensive reactive maintenances which will save a lot of cost for unplanned downtime [2].

As it is difficult to come up with general anomaly detection techniques and since different domains have different requirements regarding performance and other limitations, a wide range of methods such as Neural Networks, Rule-based techniques, Clustering, Nearest Neighbor, Statistical models and more exist. The different techniques have certain limitations what kind of anomaly they can detect (such as point, contextual or collective anomaly) and what labels need to be available. To give an example, a nearest neighbor anomaly detection would be able to detect point anomalies without needing labeled data, which makes it an unsupervised algorithm. In general it is exceptionally hard to get labeled data in some domains and therefore in such cases an unsupervised solution is preferable [1].

In this thesis the focus is on unlabeled multivariate timeseries data coming from a SINAMIC frequency converter, that is used to convert the mains voltage to a suitable signal powering an electric motor [3]. A partial timeframe of this data is declared as reference measurement and assumed to be mostly regular without anomalies. The frequency converters have a set of sensor parameters that can be extracted and processed using the Siemens Industrial Edge Device [4]. State of the art methods for this problem include calculating the distance metric between timeseries using Dynamic Time Warping and applying the Local Outlier Factor algorithm as shown in [5], using a neural network consisting of stacked autoencoders that will classify sequences based on whether they can be reconstructed by the autoencoder [2] or using a Generative Adversarial Network (GAN) including LSTMs (Long Short Term Memory) to capture temporal correlation as demonstrated in [6].

Especially Autoencoders, that encode an input into a compact hidden representation that is then decoded with the aim of reconstructing the original input, are commonly used [7]. The idea is that since the hidden representation is reduced, it will only be able to represent regular data patterns and not the patterns in anomalies. If an anomaly is fed through the autoencoder it is expected to generate a higher reconstruction error than usual. However, the approach suffers, if the training data contains anomalies, since their patterns might be learned in this case. And indeed, this applies to many real-world applications [8].

In this thesis the unlabeled data of industry motors will be analyzed and different solutions for anomaly detection in multivariate timeseries data will be tested. Since the computation power of the edge device is limited, the algorithms will be evaluated regarding their performance. Classification of anomalies would be desirable but highly depend on the application domain and is therefore not feasible in a general anomaly detection approach.

For summarization the thesis will deal with the following points:

  1. Data analysis
    (a) Statistics
    (b) (manual) identification of anomalies
  2. Development of various models for anomaly detection
  3. Model evaluation regarding to different metrics at least including the following
    (a) Accuracy
    (b) Training performance
    (c) Runtime performance
  4. Optional: Classification

[1] Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM Comput. Surv.,
41(3), jul 2009.
[2] Sean Givnan, Carl Chalmers, Paul Fergus, Sandra Ortega-Martorell, and Tom Whalley. Anomaly detection
using autoencoder reconstruction upon industrial motors. Sensors, 22(9), 2022.
[3] Siemens AG. Converter, 2022. Accessed on 14.12.2022.
[4] Siemens AG. Industrial edge / production machines, 2022. Accessed on 14.12.2022.
[5] 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.
[6] Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni.
Tadgan: Time series anomaly detection using generative adversarial networks. CoRR, abs/2009.07769, 2020.
[7] Ane Bl´azquez-Garc´ia, Angel Conde, Usue Mori, and Jos´e Antonio Lozano. A review on outlier/anomaly
detection in time series data. CoRR, abs/2002.04236, 2020.
[8] 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.