Thermal imaging and vibration-based multisensor fault detection for rotating machinery

In order to minimize operation and maintenance costs and extend the lifetime of rotating machinery, damaging conditions and faults should be detected early and automatically. To enable this, sensor streams should continuously be monitored, processed, and interpreted. In recent years, infrared thermal imaging has gained attention for the said purpose. However, the detection capabilities of a system that uses infrared thermal imaging is limited by the modality captured by this single sensor, as is any single sensor-based system. Hence, within this paper a multisensor system is proposed that not only uses infrared thermal imaging data, but also vibration measurements for automatic condition and fault detection in rotating machinery. It is shown that by combining these two types of sensor data, several conditions/faults and combinations can be detected more accurately than when considering the sensor streams individually

Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale

Utilization of two sensors in offline diagnosis of squirrel-cage rotors of asynchronous motors

In the manufacture squirrel-cage rotors of asynchronous motors, a high standard of quality is required in every part of the production cycle. The die casting process usually creates porosity in the rotor bars. This most common defect in the rotor often remains hidden during the entire assembly of the machine and is usually only detected during final testing of the motor, i.e., at the end of the production process. This leads to unnecessary production costs. Therefore, the aim is to conduct a continuous control immediately after the rotor has been cast before further processing. In our paper, we are interested in selecting a suitable method of offline rotor diagnostics of an asynchronous motor that would be effective for these needs. In the first step, the selection of the method and its integration into the overall manufacturing process is carried out. The arrangement of the sensors and their calibration is then simulated on a 2D Finite Element Model of the rotor. The proposed offline measurement procedures and technologies are finally validated by testing measurements on a rotor that simulates the most frequently occurring faults. A test system is also developed that provides the operator continuous information about the running rotor measurements and makes it easier to evaluate the quality of the cast rotor by means of graphical visualization of the faults.Web of Science1420art. no. 657

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program