To United Minds, large-scale surveys are an important offering to clients, not least the public opinion poll Väljarbarometern. A risk associated with surveys is satisficing – sub-optimal response behaviour impairing the possibility of correctly describing the sampled population through its results. The purpose of this study is to – through the use of multivariate outlier detection methods - identify those observations assumed to be non-representative of the population. The possibility of categorizing responses generated through satisficing as outliers is investigated. With regards to the character of the Väljarbarometern dataset, three existing algorithms are adapted to detect these outliers. Also, a number of randomly generated observations are added to the data, by all algorithms correctly labelled as outliers. The resulting anomaly scores generated by each algorithm are compared, concluding the Otey algorithm as the most effective for the purpose, above all since it takes into account correlation between variables. A plausible cut-off value for outliers and separation between non-representative and representative outliers are discussed. The resulting recommendation is to handle observations labelled as outliers through respondent follow-up or if not possible, through downweighting, inversely proportional to the anomaly scores.

Intelligent fault detection systems using machine learning can be applied to learn to spot anomalies in signals sampled directly from machinery. As a result, expensive repair costs due to mechanical breakdowns and potential harm to humans due to malfunctioning equipment can be prevented. In recent years, Autoencoders have been applied for fault detection in areas such as industrial manufacturing. It has been shown that they are well suited for the purpose as such models can learn to recognize healthy signals that facilitate the detection of anomalies. The content of this thesis is an investigation into the applicability of Autoencoders for fault detection in mobile robotics by assigning anomaly scores to sampled torque signals based on the Autoencoder reconstruction errors and the Mahalanobis distance to a known distribution of healthy errors. An experiment was carried out by training a model with signals recorded from a four-wheeled mobile robot executing a pre-defined diagnostics routine to stress the motors, and datasets of healthy samples along with three different injected faults were created. The model produced overall greater anomaly scores for one of the fault cases in comparison to the healthy data. However, the two other cases did not yield any difference in anomaly scores due to the faults not impacting the pattern of the signals. Additionally, the Autoencoders ability to isolate a fault to a location was studied by examining the reconstruction errors faulty samples determine whether the errors of signals originating from the faulty component could be used for this purpose. Although we could not confirm this based on the results, fault isolation with Autoencoders could still be possible given more representative signals.