## 2 Motivation  
As shown above, vibrodiagnostics and condition monitoring are closely related and represent an important and relevant domain of interest for science and practical application. However, the primary research is dedicated to the field of rotating machinery and components, for which there are validated methods of vibration signal processing in the time domain, frequency domain, and time-frequency domain [2].  
At the same time, it is evident that studies focusing on the vibrodiagnostics of sequential machines, including industrial robots, are significantly fewer than those addressing the vibrodiagnostics of rotational components. In the case of industrial robotics-related research, it is possible to see examples of successful applications of combined approaches based on deep neural networks for anomaly detection [35] and models used for audio-based fault detection [36]. There are also methods based on optical flow processing for detecting robot arm motion errors [37] and classification models based on the motion parameters of the joints of the industrial robot arm [38]. In previous research, we have also validated the ability of the LSTM neural network to classify the robot motion speed based on the vibration signal obtained from the MEMS accelerometer [39]. Within the broader field of vibrodiagnostics, industrial robot vibrodiagnostics also face similar challenges. These include difficulties in interpreting the designed models, the need for signal pre-processing, and complicated integration into real industrial environments. This is often compounded by the computational complexity and the inherently complex nature of operations performed under unstable conditions, such as load variations and variations in operating speed.  
In modern manufacturing, the field of application of industrial robots is much more extensive than just handling heavy products. It also includes applications requiring high precision, such as machining, measuring, precision assembly, etc. In precision applications, it is important to maintain stable values of the performance criteria of an industrial robot [40]. Therefore, early identification of changes in these parameters can prevent the occurrence of collision situations or the production of non-conforming products.  
The objective of our research is to contribute to the resolution of problems associated with vibrodiagnostics, with a particular focus on industrial robots. We have focused our attention on the selection of appropriate vibration signal characteristics, intending to facilitate the application of AI and ML-based models into real production environments. The selected features should be straightforward to compute, while simultaneously providing sufficient attributes to enable the detection of anomalies in the operation of an industrial robot using machine learning and artificial intelligence models.  
Based on the above, in the present paper, we consider the selection of the most effective metrics and properties of vibration signals that could be used in a follow-up implementation in a production environment for the detection of potential faults and predictive maintenance of industrial robots. The case study deals with the detection of the release of a FANUC industrial robot from its base during a handling operation for the application of the obtained knowledge in the predictive maintenance of sequential machines.  
Based on the aforementioned, the hypothesis of the given research is that, based on the selected parameters of the vibration signal, it is possible to use the selected machine learning algorithms to detect the unfastening of the robot at its base with an accuracy of at least 95%.