Belt conveyors are commonly used for the transportation of bulk materials. The most characteristic design feature is the fact that thousands of idlers are supporting the moving belt. One of the critical elements of the idler is the rolling element bearing, which requires monitoring and diagnostics to prevent potential failure. Due to the number of idlers to be monitored, the size of the conveyor, and the risk of accident when dealing with rotating elements and moving belts, monitoring of all idlers (i.e., using vibration sensors) is impractical regarding scale and connectivity. Hence, an inspection robot is proposed to capture acoustic signals instead of vibrations commonly used in condition monitoring. Then, signal processing techniques are used for signal pre-processing and analysis to check the condition of the idler. It has been found that even if the damage signature is identifiable in the captured signal, it is hard to automatically detect the fault in some cases due to sound disturbances caused by contact of the belt joint and idler coating. Classical techniques based on impulsiveness may fail in such a case, moreover, they indicate damage even if idlers are in good condition. The application of the inspection robot can “replace” the classical measurement done by maintenance staff, which can improve the safety during the inspection. In this paper, the authors show that damage detection in bearings installed in belt conveyor idlers using acoustic signals is possible, even in the presence of a significant amount of background noise. Influence of the sound disturbance due to the belt joint can be minimized by appropriate signal processing methods.

In this paper, a novel method for long-term data segmentation in the context of machine health prognosis is presented. The purpose of the method is to find borders between three data segments. It is assumed that each segment contains the data that represent different statistical properties, that is, a different model. It is proposed to use a moving window approach, statistical parametrization of the data in the window, and simple clustering techniques. Moreover, it is found that features are highly correlated, so principal component analysis is exploited. We find that the probability density function of the first principal component may be sufficient to find borders between classes. We consider two cases of data distributions, Gaussian and $\alpha$-stable, belonging to the class of non-Gaussian heavy-tailed distributions. It is shown that for random components with Gaussian distribution, the proposed methodology is very effective, while for the non-Gaussian case, both features and the concept of moving window should be re-considered. Finally, the procedure is tested for real data sets. The results provided here may be helpful in understanding some specific cases of machine health prognosis in the presence of non-Gaussian noise. The proposed approach is model free, and thus it is universal. The methodology can be applied for any long-term data where segmentation is crucial for the data processing.

The condition-based maintenance of vibrating screens requires new methods of their elements’ diagnostics due to severe disturbances in measured signals from vibrators and falling pieces of material. The bolted joints of the sieving deck, when failed, require a lot of time and workforce for repair. In this research, the authors proposed the model-based diagnostic method based on modal analysis of the 2-DOF system, which accounts for the interaction of the screen body and the upper deck under conditions of bolted joint degradation. It is shown that the second natural mode with an out-of-phase motion of the upper deck against the main screen housing may coincide with the excitation frequency or its higher harmonics, which appear when vibrators’ bearings are in bad condition. This interaction speeds up bolt loosening and joint opening by the dynamical loading of higher amplitude. The proposed approach can be used to detune the system from resonance and anti-resonance to reduce maintenance costs and energy consumption. To prevent abrupt failures, such parameters as second natural mode frequency, damping factor, and phase space plot (PSP) distortion measures are proposed as bolt health indicators, and these are verified on the laboratory vibrating screen. Also, the robustness is tested by the impulsive non-Gaussian noise addition to the measurement data. A special diagram was proposed for the bolted joints’ strength capacity assessment and maintenance actions planning (tightening, replacement), depending on clearance in the joints.

Machinery condition prognosis systems use long-term historical data to predict the remaining useful life (RUL). One of the critical steps to reach this purpose is to segment long-term data into two or several degradation stages (healthy, unhealthy, critical stage). Finding a changing point between stages may be a crucial preliminary task for further prediction of degradation process. However, finding the accurate partition into two or more stages is a challenging task in actual application when noise inherent in the observed process exhibits non-Gaussian characteristics. In this paper, a framework for data-driven segmentation is presented for prognosis of machinery long-term data in presence of heavy-tailed distributed noise with finite variance. It is assumed that three different stages are inherent in degradation process and each segment of data follows a specific trend (constant, linear, exponential or polynomial). At first, data is divided into three parts. Trend functions are fitted to the data by using robust regression method , and cumulative error is calculated. This process is done iteratively for all possible partitions into three intervals to find the segmentation which minimizes the error. The framework has been tested via empirical analysis of estimators of the changing points obtained in Monte Carlo simulations. Also, discussed approaches are applied to the real data. In such measurement, data that are commonly available (in condition monitoring systems) is aggregated from the raw signal and sampled at long intervals. Finally, effectiveness of the segmentation results is assessed by comparing them with envelope frequency analysis of raw signal to confirm the fact that detected changing points coincide with start time of the fault in the machine or not.