This paper presents the control loop data of industrial controllers that are recently made available online. All data are verified and some of it has been published previously to develop fault detection and diagnosis methods. Methods to detect faults that occur during the operation of an industrial process are important to improve profitability and have attracted attention previously. However, these methods are not always widely used in industry. One of the reasons is that any method needs to be robust and fully automated. The purpose of the data repository is to present data to test methods so that false positives and negatives are reduced to an insignificant number. Three previously published methods, oscillation detection based on the autocorrelation function, the idle index, and a method for quantization detection, together with a simple, novel saturation detection method and one new detection method are applied to all industrial data. The results are discussed along with ways to improve the robustness and automation potential of these methods.