Investigation of Machine Learning Methods for Anomaly Detection and Characterisation of Cable Shoe Pressing Processes

The ability to reliably connect electrical cables is important in many applications. A poor connection can become a fire hazard, so it is important that cables are always appropriately connected. This thesis investigates methods for monitoring of a machine that presses cable connectors onto cables. Using sensor data from the machine, would it be possible to create an algorithm that can automatically identify the cable and connector and thus make decisions on how a connector should be pressed for successful attachment? Furthermore, would it be possible to create an anomaly detection algorithm that is able to detect whether a connector has been incorrectly pressed by the end user? If these two questions can be addressed, the solutions would minimise the likelihood of errors, and enable detection of errors that anyway do arise. In this thesis, it is shown that the k-Nearest Neighbour (kNN) algorithm and Long Short-Term Memory (LSTM) network are both successful in classification of connectors and cables, both performing with 100% accuracy on the test set. The LSTM is the more promising alternative in terms of convergence and speed, being 28 times faster as well as requiring less memory. The distance-based methods and an autoencoder are investigated for the anomaly detection task. Data corresponding to a wide variety of possible incorrect kinds of usage of the tool were collected. The best anomaly detector detects 92% of incorrect cases of varying degrees of difficulty, a number which was higher than expected. On the tasks investigated, the performance of the neural networks are equal to or higher than the performance of the alternative methods