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Anomaly detection, predictive maintenance, quality assurance, and condition monitoring are buzzwords for a reason: all these practices have the potential to significantly improve the efficiency and productivity of factories thanks to industrial AI.
At the sametime, there are many challenges that need to be addressed for AI to proliferate in an industrial environment. One of the biggest challenges is the need for quality data to train AI models. The data collected by sensors in an industrial environment is often noisy, scattered,and unstructured. This makes it difficult for AI algorithms to learn from this data.
Therefore, the first step for many companies looking to make the transition to AI is to deploy the right data infrastructure and sensors to capture quality data in a robust manner. Another issue that some companies may face is the lack of the necessary skills to make the transition into AI or maintain such a system. Monitoring and maintenance of the AI system is indeed a factor to consider as the conditions in an industrial environment can change rapidly. This means that the AI models need to be monitored and able to adapt to these changes.
Despite the challenges, AI holds great promise for the industrial sector. With the right investments and partnerships, the industrial sector can overcome the challenges and improve the efficiency and productivity of production lines and industrial facilities.
The benefits of industrial AI are many and varied. Condition monitoring is one of the most well-known and well-utilized applications of AI in factories. By analyzing data collected from industrial equipment, condition monitoring can be used to detect anomalies or even predict when equipment is likely to fail. This allows companies to schedule maintenance before problems occur, preventing downtime and increasing efficiency.
Other applications of AI in a production line include quality control and process optimization. Quality control can be improved by using AI to automatically inspect products and identify defects. Process optimization can be achieved by using AI to identify inefficiencies and suggest improvements.
The challenges of AI in an industrial setting are largely due to the complex nature of factory systems. Data collection and processing can be difficult, and the variety of data types can be overwhelming. Nevertheless, despite these issues,the benefits of AI are too great to ignore.
Predictive maintenance alone can save companies millions of euros annually. As AI technology continues to develop, the potential applications in an industrial environment will only keep growing.
The industrial sector is under pressure to adopt artificial intelligence and digital technologies. PWC estimated that AI will be responsible for $15.7 trillion in annual global economic activity by 2030.
One of the biggest challenges is data collection, and future AI advances will only benefit companies that have the right infrastructure in place to collect quality data. To train AI models, large quantities of data are needed. This data can be difficult to collect in an industrial setting, as it is often scattered across different machines and devices. This essentially implies that having the right infrastructure in place to collect data is the essential step to reaping the benefits of AI.
But the good news is that AI models are becoming easier to deploy and train. With the right hardware and software infrastructure, it’s now possible to train and deploy AI models with little difficulty.
There aremany frameworks and libraries such as Keras, TensorFlow, or Pytorch that support the development of algorithms for AI applications.
For instance, Pytorch Lightning is a Python library that takes care of all the heavy software engineering and provides a high-level interface with Pytorch. With such a tool, engineers can leverage AI algorithms without having to deep dive into years of development and training.
Aside from Pytorch-lightning, there are also several toolkits that make it easy to implement AI in an industrial environment. For example, PyOD, PySAD,,and Anomalib are three open-source projects written in Python that can be used for anomaly detection.
PyOD is aPython toolbox for detecting outliers in multivariate data with 30 anomaly detection algorithms that anyone can use with a few lines of code. It offers a variety of methods for detecting outliers, such as probabilistic, linear, proximity-based, graph-based, or even based on neural networks. This typeof library is extremely powerful, as anyone can train and compare many algorithms to find the best method for a particular use case and set of data.
Another example is PySAD, a Python library for online anomaly detection that can be combined with PyOD to detect anomalies near real-time. PySAD is a lightweight library that provides methods and documentation to help detect anomalies in streaming data.
Even when it comes to computer vision, the task may seem more complicated, but fortunately, there is a large community of researchers and programmers who develop fanstastic tools like Anomalib.
Anomalib is a Python library for anomaly detection through deep learning. It provides a variety of methods to train complex algorithms with little effort. The community around this library is actively seeking to provide state-of-the-art anomaly detection algorithms, and they have proven to be very successful.
The challenges of AI in an industrial environment are many and varied. However, the potential rewards of successful AI implementation are significant. With the right approach, industrial organizations can overcome the challenges and realize the benefits of AI. Just as with any new technology, there are challenges that need to be addressed when implementing AI in an industrial environment. But, with the right data infrastructure, these challenges can be overcome, and the benefits of AI can berealized.
The trend is there, and powerful open-source AI libraries are continuously being developed. These libraries ease the process of training and maintaining complex algorithms and lower the difficulty of AI transition. The development of AI models, which took several months a few years ago, now takes only a few hours.
But remember, to embrace the power of these AI libraries, you need the right infrastructure to collect and store quality data. Stay tuned!
