Category: Know How

What is behind predictive maintenance?

While traditional maintenance involves checking systems at fixed intervals, predictive maintenance is one step ahead. Data from the machine itself, such as temperature, vibration or power consumption, is continuously recorded and analyzed. With the help of algorithms and AI models, conclusions can be drawn as to when a component is likely to fail. This means that maintenance can be carried out exactly when it is really necessary, i.e. not too early and not too late.

According to the Industry of Things, predictive maintenance describes a data-based maintenance approach in which sensor values are analyzed and failure probabilities are calculated on this basis in order to avoid unplanned downtimes.

How does this work in practice?

The basic principle is simple: machines are equipped with sensors that continuously supply data. This data is collected, processed and analyzed either directly in the machine or via an edge system.

The procedure is usually as follows:

1. Data acquisition: Sensors measure vibrations, temperatures, voltages, etc.
2. Data transmission: The information is passed on via secure networks or IoT gateways.
3. Analysis: AI models or machine learning algorithms recognize patterns, anomalies or trends.
4. Forecast: If the system detects deviations from the normal state, a maintenance requirement is reported in good time before a failure occurs.

For these processes to function smoothly, reliable hardware is needed at the edge. After all, not all data can or should always be sent to the cloud – low latency times, data protection and real-time requirements often make local processing necessary.

This is where mini PCs like our CORE 5 Ultra come into play: thanks to the Intel® Core™ Ultra 5 125U processor (with 2 Performance Cores & 10 Efficiency Cores) and integrated NPU with 11 TOPS, it can run AI models directly on the spot. This allows sensor data to be analyzed in real time and potential faults to be detected before they occur.

Advantages for industry and users

The advantages of predictive maintenance are obvious:

• Less downtime: Unplanned downtimes are drastically reduced
• Lower maintenance costs: components are only serviced or replaced when necessary
• Longer service life: machines are protected because wear is detected at an early stage
• More efficient planning: maintenance work can be carried out in a targeted and plannable manner
• More transparency: companies gain valuable insights into the condition of their systems

Predictive maintenance can bring enormous efficiency gains, particularly in sectors such as production, mechanical engineering, energy and transportation. This makes it a central component of modern Industry 4.0 strategies.

What is needed for successful implementation?

Many companies would like to introduce predictive maintenance, but don’t know exactly where to start.

Fraunhofer IESE identifies three key success factors: good data, suitable models and well thought-out integration into existing processes.

1. Data basis: A reliable model cannot be created without high-quality and sufficient historical data.
2. Analysis models: AI or machine learning algorithms must be continuously fed with new data and validated.
3. Integration: The results must be visualized clearly and integrated into the maintenance process, ideally automatically via existing systems.

For predictive maintenance to make economic sense, companies should start small. For example, with a pilot project on a critical machine, clearly defined goals and a scalable infrastructure. The system can then be transferred to other systems step by step.

Hardware at the edge – the key to real time

A key success factor is the right hardware platform. This is because predictive maintenance applications require stable systems that run continuously, even in harsh industrial environments.

Our spo-comm industrial PCs are just right for this! They offer high computing power in the smallest of spaces, are temperature-resistant and durable in continuous operation. With integrated AI functions, for example via NUPs or optional accelerator cards, they can process sensor data directly without having to go via the cloud.

This saves bandwidth, reduces latency and makes the solution secure and independent – ideal for edge AI scenarios in industry.

Challenges and limits

As great as the advantages are, they do not come without challenges:

• Data quality: Sensor errors or incomplete data make it difficult to make accurate forecasts.
• Complexity: Different machine types and operating conditions require flexible models.
• Know-how: Implementation requires experience in data analysis, AI and industrial IT.
• Costs: Setting up a suitable infrastructure can be costly at the beginning, but in the long term it becomes automated due to less downtime.

However, companies that take a strategic approach to this issue and adapt their processes accordingly will quickly benefit from more stable workflows and lower maintenance costs.

From reaction to prevention

Predictive maintenance is more than just a buzzword, it is a real game changer for the industry. The use of modern sensor technology, AI and edge computing is turning reactive maintenance into an intelligent, data-driven process. Systems are not only monitored, but increasingly understand themselves when they need support.

With robust and powerful mini PCs such as the CORE 5 Ultra from spo-comm, such systems can be implemented reliably – right where the data is generated.

In the era of Industry 4.0 and increasing digitalization, it is easy to be dazzled by buzzwords such as IoT, cloud or AI. However, SCADA systems (Supervisory Control and Data Acquisition) are still the backbone of many industrial processes. They enable companies to efficiently monitor, control and optimize production facilities, energy supply networks and infrastructures. “Back to basics” here means understanding the core of these systems, clearly recognizing how they work, the components and the benefits for B2B decision-makers. In this article, you will learn in a practical way why SCADA is still a crucial building block for industrial automation today.

Understanding SCADA systems: Definition and architecture

SCADA is a process control system that collects, processes and displays data from the real world to support human decision-making and automation. At their core, SCADA systems consist of several levels that work together seamlessly. Sensors and actuators record physical variables such as temperature, pressure, flow or fill levels and execute control commands. This data is forwarded to programmable logic controllers (PLCs) or remote terminal units (RTUs), which process it and send control instructions back to the actuators as required.

The system is operated via human-machine interfaces (HMI), i.e. graphical user interfaces that provide operators with insights into the current process status. Central servers store the data long-term and serve as historians to identify trends, create reports and uncover optimization potential. Communication between the components takes place via industrial networks and protocols such as OPC, Modbus or DNP3, enabling real-time control and remote monitoring.

Typical areas of application

SCADA systems can be used in a variety of ways in industry. In the manufacturing industry, they monitor production lines, detect anomalies at an early stage and ensure smooth processes. This allows maintenance work to be planned in advance and quality problems to be avoided. In the energy supply sector, SCADA plays a central role in the control of electricity, gas and water networks. Operators receive a complete overview of load distribution, consumption patterns and faults, which minimizes outages and maximizes efficiency.

SCADA is also indispensable in infrastructure: traffic control, water and wastewater systems or the monitoring of critical building and environmental systems are reliably controlled by it. With Industry 4.0, the integration of SCADA with Industrial IoT, cloud services and AI is becoming increasingly important. Modern systems use this networking to intelligently optimize production processes and make quick decisions based on extensive data analyses.

Challenges in today’s SCADA world

Digitalization increases the demands on SCADA systems. Companies expect scalable and modular systems that can keep pace with growing production requirements. Networking with cloud platforms and IoT devices opens up new possibilities, but significantly increases the attack surface for cyber threats. IT/OT convergence poses a particular challenge, as IT security concepts are not always directly transferable to operational technology. Standardized interfaces and protocols as well as specialized security measures are therefore essential to ensure secure and efficient processes.

In addition, integration into modern Industry 4.0 environments requires a high degree of flexibility in order to be compatible with cloud services, ERP systems or mobile applications. Edge computing is increasingly being used to process data directly at the source, reduce latency times and conserve bandwidth. This means that systems remain efficient and reliable even in networked environments. (Source: Inductive Automation)

Benefits of SCADA in the B2B context

The advantages of SCADA systems for companies are manifold. They enable significant process optimization, as processes can be continuously monitored and adjusted in real time. Early fault detection reduces unplanned downtime and lowers maintenance costs. At the same time, the systems create transparency: all relevant process data is traceable and can be used for optimization and well-founded decisions. Another key advantage is remote monitoring: production managers and IT managers can monitor systems from any location and intervene if necessary. The evaluation of historical data not only supports companies in complying with regulatory requirements, but also in identifying efficiency potential. This makes SCADA an indispensable tool for the digitalization of industrial processes. (Source: MixMode)

Trends and outlook

The technology is constantly evolving. Mobile HMI applications enable operators to monitor processes while on the move. Cloud SCADA solutions enable central data storage and analysis across multiple locations, while edge computing optimizes processing directly on site. Artificial intelligence is increasingly being used to efficiently evaluate large volumes of data, predict maintenance requirements and autonomously optimize processes. At the same time, the importance of cyber security is growing: special solutions protect systems from attacks and safeguard the integrity of critical processes. For industrial decision-makers, this means SCADA remains a core component of automation, but must be continuously modernized and adapted to new requirements in order to achieve the greatest possible benefit.

Conclusion: SCADA as the key to Industry 4.0

These systems are more than just traditional process control systems. They form the backbone of industrial automation, create transparency, enable efficiency gains and are indispensable for the implementation of Industry 4.0 strategies. For production managers, IT managers and technology managers, it is crucial to future-proof SCADA solutions in order to take full advantage of digitalization, remote monitoring and Industrial IoT. Understanding SCADA and using it strategically lays the foundation for stable, efficient and flexible production processes in the B2B environment.

Curious?

Do you have any questions about applications or our products? Do not hesitate to contact us or arrange a consultation directly.

What is an NPU?

A Neural Processing Unit is a processor that has been specially optimized for the calculation of neural networks and AI models. Unlike a CPU, which can be used universally, or a GPU, which was developed for graphics-intensive and parallel computing processes, the NPU focuses exclusively on AI tasks such as pattern recognition, speech analysis or image processing.

Its architecture is designed to perform mathematical operations for machine learning particularly quickly and energy-efficiently. This makes it possible to perform complex AI calculations directly on the device (on-device AI) – without having to outsource data to the cloud. More about the differences between CPU, GPU and the Neural Proceccing Unit here.

Advantages for companies in the B2B sector

The NPU offers several strategic advantages for the B2B sector:

• Energy efficiency: They consume significantly less power than GPUs with comparable AI performance. This reduces operating costs and makes them easier to use in mobile devices or edge systems.
• Low latency: As data is processed locally, transmission times to the cloud are eliminated. This is crucial for real-time applications such as production monitoring or video analysis.
• Data security: Sensitive data – for example from production, medical devices or financial transactions – does not leave the company. This makes it easier to adhere to data protection and compliance requirements.
• Compact integration: The AI accelerators cannot be integrated directly into system-on-chip architectures, making them ideal for IoT devices, embedded systems and industrial PCs.

Possible disadvantages and restrictions

Despite their advantages, NPUs are not a universal solution for every computing task. Companies should bear a few points in mind:

• Specialization: They are focused on AI tasks and cannot replace general computing tasks.
• Software support: Full performance can only be achieved if applications and frameworks are optimized for it.
• Initial investment: Even if the investment pays off in the long term, it often requires new generations of hardware or upgrades to existing systems.

Areas of application in the B2B environment

NPUs can be used in a wide range of applications across numerous industries.

In Industry 4.0, for example, they enable real-time analysis of sensor data in order to detect production errors at an early stage and avoid downtime. This significantly increases quality and efficiency in production.

In the healthcare sector, they are used for local image analysis of MRI or X-ray images. This speeds up diagnoses while sensitive patient data remains in-house and strict data protection regulations are adhered to.

Their strengths are also played out in the financial sector: they support fraud detection by analyzing transaction patterns in real time and reporting potential irregularities immediately.

In logistics and retail, NPUs help with automated inventory control, the analysis of customer flows and intelligent route planning. Companies benefit from optimized processes and better use of resources.

NPUs can even create added value in corporate communications – for example through real-time translations and transcriptions in video conferences without the need for external servers or cloud services.

Find out how you can also use our spo-comm Mini-PCs for AI here:

Future prospects

Market experts expect NPUs to become standard in business PCs and workstations within the next two to three years. Manufacturers such as Intel, AMD, Apple and Qualcomm are already integrating them into current product lines. Similar to GPU acceleration a few years ago, the NPU will thus become an integral part of corporate IT. the big difference: NPUs make it possible to use AI functions without significant energy consumption and independently of cloud infrastructures. This opens up new possibilities for smart, mobile and data-secure business solutions. More on the upcoming developments in AI PCs was explained in more detail by heise.de.

Our conclusion on NPUs in the B2B sector

The Neural Processing Unit is more than just another hardware component – it is a strategic building block for future-proof IT architectures. Companies that rely on NPU-supported systems at an early stage benefit from more efficient AI processing, more data protection and greater independence from cloud services.

At a time when artificial intelligence is becoming a decisive competitive factor, the NPU can make the difference – between an IT infrastructure that only “supports” AI and one that consistently integrates and scales it.

Do you have any questions about AI applications with Mini PCs or would you like a free, no-obligation consultation about one of our products or your application? Then don’t hesitate to contact us!

What is SATA anyway?

SATA stands for Serial Advanced Technology Attachment. It is an interface for data transfer between storage media (e.g. hard disks or SSDs) and computers or servers. The decisive advantage over the older Parallel ATA (PATA) standard lies in the serial transmission, which enables higher speeds and requires fewer cables and pins.

According to ComputerWeek, the technology was developed to meet the increasing demands on data transfer rates. While PATA had to make do with a maximum of 133 MB/s, the first generations started at 150 MB/s and increased to up to 600 MB/s with generation three. In most cases, this speed is still perfectly adequate for everyday business use.

The interfaces generally use a simple seven-pin cable for data transmission and a separate power cable – which not only improves the airflow in the housing, but also makes maintenance in server racks easier. This cable and port efficiency is a clear advantage over older solutions, especially for large storage arrays.

SATA in everyday B2B life: a solid basis instead of hype

IT investments must remain economically viable, especially in the B2B sector. Not every company needs the extreme speeds or IOPS values (Input/Output Operations Per Second) that NVMe or PCIe-based SSDs offer. This is where SATA comes into its own:

• Cost savings:
  SATA SSDs are significantly cheaper per gigabyte than NVMe models. For large storage capacities in servers or storage systems, this can quickly lead to considerable savings.
• Compatibility:
  The technology can be used universally. Both older and new systems support the interface, which facilitates integration and migration.
• Reliability:
  For many workloads – such as archiving, backup, file servers or less latency-critical databases – SATA offers sufficient speed with high stability.

As Lenovo explains, SATA is still widely used because it simply “works”. The technology has been tried and tested for over two decades, making it a safe choice for companies that value mature solutions.

Another argument: MTBF (Mean Time Between Failures) – i.e. the average downtime – is sometimes over 1.5 million hours for high-quality SSDs. This means very high operational reliability, especially for constantly running systems in continuous operation such as video surveillance, archive servers or system monitoring.

The different generations at a glance

Especially in the B2B context, it is worth knowing the differences between the standards. Here is a brief overview:

With the introduction of NCQ in the second generation, an important foundation was laid for modern multi-threading applications. NCQ allows the drive to process commands in an optimized sequence, which leads to better performance, especially in multi-user systems and when database processes are accessed in parallel. This function is also fully retained in generation three.

The third generation is still sufficient for many applications today, such as large file servers, NAS systems or backup solutions. The technology really comes into its own with sequential data access – typical for backup or streaming.

SATA vs. NVMe: Does it always have to be the fastest?

NVMe is undoubtedly the top dog when it comes to maximum speed. Anyone who operates databases with extremely high transaction rates or hosts virtual machines on a large scale will appreciate the advantages of NVMe. But especially in the B2B segment, the question is often: is the extra cost worth it?

Not every workload benefits from the higher performance. Many classic company applications such as office file servers, e-mail archives or DMS systems (document management) do not require gigantic data rates. Here, SATA remains an extremely attractive solution.

A typical practical example: In a medium-sized company with around 100 employees, around 10 TB of backup data is stored every week. Here, SATA-based systems offer an optimum ratio of storage capacity to price – without speed becoming a bottleneck.

In 2023, over 40% of German companies will still primarily rely on SATA hard disks and SSDs for their on-premises storage solutions – especially for long-term data storage and backup.
Energy consumption also plays a role: the drives generally consume less power than higher-performance NVMe drives. In data centers, this can mean considerable cost savings across many drives.

This point is becoming increasingly important, especially when it comes to green IT. While NVMe SSDs often require active cooling due to their high performance, SATA solutions usually manage without additional measures – which not only saves energy, but also reduces the overall complexity of the infrastructure.

Conclusion: Back to basics is worth it!

Especially in the B2B environment, technology should not only shine on paper, but also be economical and reliable. SATA may seem old compared to NVMe, but it is still a solid, cost-conscious basis for numerous business applications. Anyone looking at all the options for a hardware refresh should by no means write off SATA prematurely.

If you want to achieve the ideal balance between performance and costs, you can rely on tiered storage architectures – i.e. systems that combine fast NVMe SSDs for real-time access and slower SATA SSDs for cold storage. In this way, modern requirements can be efficiently solved with proven technology.

Discover the variety of spo-comm Mini-PCs, whether with SATA, NVMe or both: