Strain Virtual Sensing Applied to Industrial Presses for Fatigue Monitoring.

The techniques that allow one to estimate measurements at the unsensed points of a system are known as virtual sensing. These techniques are useful for the implementation of condition monitoring systems in industrial equipment subjected to high cyclic loads that can cause fatigue damage, such as industrial presses. In this article, three different virtual sensing algorithms for strain estimation are tested using real measurement data obtained from a scaled bed press prototype: two deterministic algorithms (Direct Strain Observer and Least-Squares Strain Estimation) and one stochastic algorithm (Static Strain Kalman Filter). The prototype is subjected to cyclic loads using a hydraulic fatigue testing machine and is sensorized with strain gauges. Results show that sufficiently accurate strain estimations can be obtained using virtual sensing algorithms and a reduced number of strain gauges as input sensors when the monitored structure is subjected to static and quasi-static loads. Results also show that is possible to estimate the initiation of fatigue cracks at critical points of a structural component using virtual strain sensors.

Strain Virtual Sensing for Structural Health Monitoring under Variable Loads.

Virtual sensing is the process of using available data from real sensors in combination with a model of the system to obtain estimated data from unmeasured points. In this article, different strain virtual sensing algorithms are tested using real sensor data, under unmeasured different forces applied in different directions. Stochastic algorithms (Kalman filter and augmented Kalman filter) and deterministic algorithms (least-squares strain estimation) are tested with different input sensor configurations. A wind turbine prototype is used to apply the virtual sensing algorithms and evaluate the obtained estimations. An inertial shaker is installed on the top of the prototype, with a rotational base, to generate different external forces in different directions. The results obtained in the performed tests are analyzed to determine the most efficient sensor configurations capable of obtaining accurate estimates. Results show that it is possible to obtain accurate strain estimations at unmeasured points of a structure under an unknown loading condition, using measured strain data from a set of points and a sufficiently accurate FE model as input and applying the augmented Kalman filter or the least-squares strain estimation in combination with modal truncation and expansion techniques.

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends.

Manufacturing companies increasingly become "smarter" as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis.

To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis.