VPF STMicroelectronics Intro (LC)

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ST is a global semiconductor leader delivering intelligent and energy-efficient products and solutions that power the electronics at the heart of everyday life. ST’s products are found everywhere today, and together with our customers, we are enabling smarter driving and smarter factories, cities and homes, along with the next generation of mobile and Internet of Things devices. By getting more from technology to get more from life, ST stands for life.augmented. 

In 2019, the Company’s net revenues were $9.56 billion, serving more than 100,000 customers worldwide. Further information can be found at

Condition Monitoring and Predictive Maintenance at a glance

Condition-based Monitoring (CbM) and Predictive Maintenance (PM) are two maintenance strategies which aim at optimizing equipment efficiency and reducing service timing and costs during equipment lifecycle.

Condition Monitoring (CM) is the monitoring of several parameters such as equipment vibration and temperature to identify potential issues such as misalignments or bearing failures. Condition monitoring tools can for instance map equipment degradation when a vibration analysis shows a change in the harmonic frequency of rotating equipment components. Frequency analyses can be based both on vibrometer and microphone data.

Continuous Condition Monitoring techniques can be applied on several pieces of equipment such as compressors, pumps, spindles and motors and can also be implemented to identify partial discharge on machine or vacuum leaks.

Predictive Maintenance is based on Condition Monitoring, abnormality detection and classification algorithms, and integrates predictive models which can estimate the remaining machine runtime left, according to detected abnormalities. This approach uses a wide range of tools, such as statistical analyses and Machine Learning to predict the state of the equipment.

Employee monitoring processes

From Condition Monitoring to Predictive Maintenance: the role of Edge Processing

From designing smart sensor nodes and configuring embedded software running in sensor nodes and the gateway, to developing software to be integrated in the Cloud or company Enterprise Resource Planning (ERP) system, Predictive Maintenance requires a variety of skills and competencies. Machine Learning and Artificial Intelligence algorithms can be implemented to ensure technical abnormalities are detected early and equipment uptime is maximized.


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DC Fast Charging Station

The increasing number of Electric Vehicles (EVs) on the roads brought about the need for more charging stations, including DC fast charging stations and CHAdeMO-compliant fast charging stations.

DC fast charging stations provide vehicles with level-3 charging, enabling a 100-kilometer driving range in just 10 to 12 minutes. While level-2 chargers supply AC power to the battery (which is then converted to DC power by the on-board charger), level-3 fast chargers directly deliver DC power, which significantly improves charging time.

DC charging stations currently target three main power levels: ≤ 50 kW for city use, 150 kW for highways, and 350 kW for supercars/trucks/buses. DC fast chargers usually include 20 kW subunits and then stack them up to create a higher power DC charging system. The ≤ 50kW power level category for city use shows the biggest growth according to market studies.

Two EV chargers close up

ST's offer for DC fast charger designs

ST provides a range of power discretes including Silicon-Carbide (SiC) and silicon power MOSFETs, diodes and protection devices (TVS), and isolated gate drivers, as well as high-performance STM32 microcontrollers to contribute to the development of high-efficiency, high-power density DC fast charging stations.

Our CAN transceivers are designed to boost connection between an electric car and the charging pile, and the power line transceivers and programmable Bluetooth Low Energy System on Chips ensure fast connection between the charging pile and the user interface.

DC Fast Charging Station block diagram


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