## 1 Introduction
In the second case, however, there is a need to use a device to manage the acquisition of data from extra sensing units installed on the robot (for instance, the accelerometer, and the acoustic and laser sensors). As is easy to imagine, in the industry, the choice falls on the first solution as less expensive, given that it does not require other external components. Moreover, using the controller's data allows for the definition of a common strategy valid in all systems which use that controller. Finally, the communication protocols used to exchange information with controllers are robust, and thus with a lesser possibility of losing data. Based upon these considerations, this work presents in Section 3.1 an ROS-based CM architecture running in a Linux system which enables the possibility of obtaining signals from industrial controllers. * **Acquire, collect and store data. ** The common questions that we need to answer to obtain a consistent dataset from which to extract information for further analysis are the following: How to manage the data acquisition phase? Which rules do we need to use to collect, organize and store data? To answer these questions, there is a need to go deeper into the details of a cobot application; however, it is easy to understand that it is not trivial to decide on a strategy to acquire data in a system that performs different combinations of movements, reaching many different positions at different speeds, sometimes carrying different payloads, all in the same program. In fact, those systems are thought of for flexible production and may be used for many different tasks contemporary. Therefore, in Section 3.3, we propose a methodology to solve these issues and we test its validity in a case study application common in Industrial scenarios in Section 4. * **Analyze and detect abnormal data. ** This area is the one that affects the reliability of a CM system, and the related question is: Which method do we need to use to automatically extract information on the system's health? Furthermore, answering this question is not straightforward since there lots of valid techniques have been proposed in the literature. Generally, these are divided into model-based, signal-based and data-driven and they have proven their validity in many different scenarios. Fundamentally, we need to keep in mind that the methodology chosen should be the one that decreases the number of missed and false fault detection. However, this also depends on the kind of failure in the system we want to detect. In this paper, regarding the case study, we proposed a solution based on common health indicators (HIs) that are well-known in this area, considering that the purpose of this paper is to define a general CM architecture which can be applied to different collaborative robots involved in flexible manufacturing. For this reason, this work is not going to be focused on defining the best strategy for detecting failures but on how to automate this process. The following sections will be devoted to expanding the debate about these common issues regarding fault detection in collaborative manipulators. The methodology chosen in this work to assess manipulator health will be presented after introducing recent arts in this context in Section 2. While the developed CM architecture will be discussed in Section 3, it also proposes a systematic criterion for organizing data in order to obtain reliable algorithms for automatic fault detection in cobots. In summary, this work aims to address these previously introduced problems by proposing a new Condition Monitoring architecture. This solution is valid for defining an automatic failure detection strategy in industrial collaborative robots to support flexible manufacturing. The CM architecture is based on the ROS framework with the potential of being adaptable for many different industrial cobot brands. In particular, the studies in this paper were conducted to test the CM architecture on an Omron TM cobot.