What you’ll learn:
- Why is Industry 4.0 still fragile?
- What do we need to build intelligent automation?
In the 21st century, the industry entered its fourth revolution: Industry 4.0. While this is an incredible improvement over previous industrial revolutions, it’s still inefficient and fragile.
For example, nearly half of global waste is caused by manufacturing inefficiencies: building the wrong mix of products; building products too early, expiration of products or components; lack of connection between supply chains, manufacturing, and ordering; and even inefficient delivery routes. The World Economic Forum estimates that up to 15% of trucking miles are currently driven with no load, wasting fuel and time.
Inefficiency isn’t the only issue facing the manufacturing industry. Ransomware remains a top threat for all companies. These attacks have increased 278% since 2019, with both costly and deadly consequences.
Global supply chains are bearing the brunt of ransomware attacks, according to a new report that found manufacturing was the most targeted industry during 2021. For example, when attackers went after the largest pipeline system for refined oil products in the U.S. in 2021, it caused a five-day shutdown and $4.5 billion ransomware payment, wreaking havoc across the supply chain.
Safety is another concern. Ensuring workers go home safely each night, and want to return to work the next day, is essential for any industry. The National Safety Council estimates the total cost of work injuries in 2022 to be $167.0 billion. This figure includes wage and productivity losses of $50.7 billion, medical expenses of $37.6 billion, and administrative expenses of $54.4 billion.
Factory downtime and inefficiencies create additional costs for the manufacturing industry. A report from Siemens estimates that the cost of downtime for an average large plant is $129 million per year.
Key Enablers of Smart and Efficient Factories
It’s no surprise that the above challenges are driving the need for more intelligence at the factory. Flexible and smart factories are the path to resiliency and efficiency.
What do we need to build intelligent automation? To enable a smart factory, some of the basic required elements include:
- Edge computing: Scalable platforms that can be deployed in different applications that enable real-time analytics and decision-making at the edge, unhampered by network latency and reducing data center and network cost.
- Machine learning: Ability to teach machines to take on more complex tasks under changing conditions.
- End-to-end security: Required to protect data and assets from external attack, as everything will be connected to allow for collaboration across and between manufacturing sites.
- Real-time communication: Separating operational technologies that need real-item control, while enabling larger datasets to be exchanged between machines at the edge.
Though the above features are a necessity, they don’t solve the problem of reconfiguring the factory floor every time the market’s or the customer’s requirements change. To deal with the high level of end-product variance, it’s highly desirable that the manufacturing equipment be flexible and configurable.
While flexibility and configurability are achievable at the higher levels of the smart factory hierarchy (Fig. 1), which are already designed to be modular (e.g., servers, routers), it’s much more complicated when going to the physical resources layer. There, the factory equipment needs to interface with the sensors that collect the real-world physical quantities.
For example, one requirement for flexibility is the possibility to accept any sensor output without having to change the analog front-end that converts the analog sensor signal into the digital processing world.
In every factory, there are hundreds, if not thousands, of sensors with their associated analog front ends (AFEs). Each AFE is optimized to its sensor and could be measuring a voltage, current, or resistance. For instance, an AFE could be measuring a temperature, with its very small millivolt signal, while another AFE could be measuring a ±25-V large voltage.
When some of the sensors need to be replaced, say, due to reconfiguring the factory floor, the measurement unit that includes the AFE will also need to be replaced. This means halting the production process while the factory floor is being reconfigured.
The obvious solution to the problem of replacing the AFE for every sensor change is to have software-configurable universal AFEs. Such an AFE can measure a voltage one day and then, if the sensor output is a current, the AFE can be reprogrammed to measure a current.
Reconfigurable Analog Front-Ends Lead to a Smarter Factory
How does this work in practice? NXP’s NAFE family of analog front-ends illustrates one example. With its universal reconfigurable inputs, the AFE can be connected to most available sensors, whether the measurement is a voltage, current, or resistance. In addition, the wide 180-dB dynamic range means that the AFE can measure voltages as low as nanovolts and as high as ±25 V with the same 0.01% accuracy.
Not only does the AFE measure a variety of sensor signals with 24-bit resolution and 0.01% accuracy, it includes many of the discrete components that are usually on the measurement board. These include high voltage protection, high-voltage fast multiplexers, sensor voltage and current excitation, and low-drift voltage references, in addition to a slew of diagnostic and supply supervisory circuitry for condition monitoring and anomaly detection (Fig. 2).
Because the inputs of the NAFE are software-programmable, reconfiguring the factory floor is as simple as reprogramming the measurement unit remotely. This eliminates the time needed to replace the various measurement units, as well as the downtime of the factory.
NXP Smart Sensors Support Predictive Maintenance
The advanced diagnostic features integrated within the NAFE family enable factory operators not only to detect small anomalies, but also to predict and prevent any issues before they occur to avoid unexpected downtime and plan timely maintenance. Some of the advanced diagnostic features include open and short detection, cable and mounting degradation, temperature, power supply, reference clock, input signal anomalies, component aging, reference drift, and others.
Detecting the small anomalies in the presence of large signals is only possible due to the wide dynamic range of the NAFE family, in addition to its high accuracy. These advanced diagnostic features also facilitate the implementation of functional safety.
Predicting failure requires the diagnostics features mentioned above, as well as powerful edge processors with the right algorithms, models, and artificial intelligence. The NAFE family of AFEs, coupled with NXP’s crossover MCUs such as the RT1080 family, or MPUs like the i.MX family, address reliability, security, and safety concerns.
Conclusion: Smart Manufacturing Efficiency Improved by Flexible AFEs
Spending on smart manufacturing is expected to grow from $345 billion in 2021 to more than $950 billion in 2030, according to ABI Research. Flexible automation offers manufacturers much-needed resilience in an ever-changing environment. The right analog front-end can help increase efficiency by allowing quick reconfiguration of the factory floor based on shifting needs.
On this front, the NAFE family of AFEs featuring fully software configurable inputs allows the measurement of various sensors without hardware changes. Coupled with NXP’s processors, security, and functional-safety solutions, the built-in diagnostic features of the NAFE family help facilitate implementation of predictive maintenance, anomaly detection, and failure prediction, in addition to the required safety and security.
