A novel method for diagnosing rolling bearing surface damage by electric impedance analysis

This paper presents a novel condition monitoring method for rolling bearings, based on measuring the electric bearing impedance. The method can diagnose the presence of damage by frequency-domain analysis, and its extension along the raceway by time-domain analysis. The latter enables the assessment of the severity and the progression of bearing damage. A fatigue test shows that the occurrence of pittings in the bearing raceways causes characteristic peaks in the impedance signal, and that the duration of the peaks increases during damage progression. A second test series with artificial damage shows that the duration of the peaks depends on the bearing load and the length of the damage along the raceway and confirms the explanation hypothesis.

Diagnostics of the drive shaft bearing based on vibrations in the high-frequency range as a part of the vehicle's self-diagnostic system

Currently, one of the trends in the automotive industry is to make vehicles as autonomous as possible. In particular, this concerns the implementation of complex and innovative selfdiagnostic systems for cars. This paper proposes a new diagnostic algorithm that evaluates the performance of the drive shaft bearings of a road vehicle during use. The diagnostic parameter was selected based on vibration measurements and machine learning analysis results. The analyses included the use of more than a dozen time domain features of vibration signal in different frequency ranges. Upper limit values and down limit values of the diagnostic parameter were determined, based on which the vehicle user will receive information about impending wear and total bearing damage. Additionally, statistical verification of the developed model and validation of the results were performed.

Development of damage detection parameters over the lifetime of a rolling element bearing

This paper presents the trends of damage detection parameters over the lifetime of a rolling element bearing. In the experimental part, a series of bearing tests was performed using the twin-disc test device, until the monitored bearing was severely worn. This was followed by the analysis of measured acceleration and acoustic emission data in a constant-load condition, but also as loaded with impact-type loading. The results showed that traditionally used parameters, such as kurtosis and RMS, can indicate whether the bearing is damaged or not in a non-impact load condition. However, especially under impact-loading, the parameters based on acoustic emission data showed good performance and enabled monitoring of progress of the bearing damage.

An approach to investigating rolling element bearing damage under impact loading

This paper presents an approach to studying rolling element bearing damage under the interference of impact loading. In the experimental part, a series of bearing tests was performed by using the twin-disc test device with artificially damaged bearings. This was followed by analysis of the measured acceleration response data in impact-free condition as well as under the influence of the impact loading. The results showed successful detection of the bearing outer race damage by using typical bearing damage detection approaches regardless whether the impact loading was applied to the system or not. In turn, recognition of the bearing rolling element damage required specific signal processing.

Analisa Kerusakan Pompa OCCWP Pada Pembangkit Listrik Tenaga Uap 2 x 115 MW

The use of pumps in the industrial world, especially in power plants, cannot be separated from problems that arise that can cause losses. The research was conducted at PLTU 2 X 115 MW by taking a case that had happened to the pump, namely the Open Cycle Cooling Water Pump 2A. The analytical method used in this final project is the vibration analysis method, namely by reading the spectrum of the data retrieval results so that it can be seen what happens to the equipment that experiences an increase in vibration. Bearing damage can be seen in the spectrum image of the measurement results, where the appearance of the spectrum in the high frequency range is an indication of bearing damage. And the high vibration which reaches 13.06 mm/s and is already in the danger category based on ISO 10816-3.

A CapsNet-Based Fault Diagnosis Method for a Digital Twin of a Wind Turbine Gearbox

Accurate fault diagnosis of complex energy systems, such as wind turbines, is essential to avoid catastrophic accidents and ensure a stable power source. However, accurate fault diagnoses under dynamic operating conditions and various failure mechanisms are major challenges for wind turbines nowadays. Here we present a CapsNet-based deep learning scheme for data-driven fault diagnosis used in a digital twin of a wind turbine gearbox. The CapsNet model can extract the multi-dimensional features and rich spatial information from the gearbox monitoring data by an artificial neural network named the CapsNet. Through the dynamic routing algorithm between capsules, the network structure and parameters of the CapsNet model can be adjusted effectively to realize an accurate and robust classification of the operational conditions of a wind turbine gearbox, including front box stuck (single fault) and high-speed shaft bearing damage & planetary gear damage (coupling faults). Two gearbox datasets are used to verify the performance of the CapsNet model. The experimental results show that the accuracy of this proposed method is up to 98%, which proves the accuracy of CapsNet model in the case study when this model performed three-state classification (health, stuck, and coupled damage). Compared with state-of-the-art fault diagnosis methods reported in the literature, the CapsNet model has a competitive advantage, especially in the ability to diagnose coupling faults, high-speed shaft bearing damage & planetary gear damage in our case study. CapsNet has at least 2.4 percentage points higher than any other measure in our experiment. In addition, the proposed method can automatically extract features from the original monitoring data, and do not rely on expert experience or signal processing related knowledge, which provides a new avenue for constructing an accurate and efficient digital twin of wind turbine gearboxes.

Bearing Severity Fault Evaluation Using Contour Maps—Case Study

Increasing industrial competitiveness has led to an increased global interest in condition monitoring. In this sector, rotating machinery plays an important role, where the bearing is one of the most critical components. Many vibration-based signal treatments are already being used to identify features associated with bearing faults. The information embedded in such features are employed in the construction of health indicators, which allow for evaluation of the current operating status of the machine. In this work, the use of contour maps to represent the diagnosis map of a bearing, used as a health map, is presented for the first time. The results show that the proposed method is promising, allowing for the satisfactory detection and evaluation of the severity of bearing damage. In this initial stage of the research, our results suggest that this method can improve the classification of bearing faults and, therefore, optimise maintenance processes.