Artificial intelligence (AI) is reshaping the manufacturing landscape, ofering opportunities for eficiency improvements and innovation. Through Machine Learning (ML) and Deep Learning (DL), AI enables predictive maintenance, anomaly detection, and image analysis in industrial settings. ML algorithms empower systems to learn from data, facilitating predictive maintenance by predicting optimal equipment servicing schedules based on operational conditions. DL techniques, including Convolutional Neural Networks (CNNs), are revolutionizing industrial image analysis by extracting intricate features for quality control and defect detection. Moreover, the integration of DL with natural language processing (NLP) streamlines tasks like document analysis and inventory management. At the University of Naples Federico II's CINI-AIIS Lab, cutting-edge AI projects are underway, showcasing the transformative potential of AI in the industry sector.
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Artificial intelligence (AI) is transforming various industries, including the manufacturing sector, by emulating human intelligence to tackle complex challenges. In the industrial domain, AI holds significant promise, enhancing operational eficiency, optimizing processes, and drivthe CINI-AIIS Lab, highlighting their innovative contributions. 2. Prediction and Forecasting for railway rolling stock equipment ing innovation. AI-powered systems can analyze exten- The manufacturing industry is currently undergoing the sive datasets to detect anomalies, predict equipment failso-called Industry 4.0 revolution, characterized by the exures, and improve overall productivity. Machine Learn- tensive integration of physical and digital realms within ing (ML), a subset of AI, enables systems to learn from data and make informed decisions, such as predicting production settings. Key technologies driving this revolution include the Industrial Internet of Things, Big Data, optimal maintenance schedules based on equipment con- Artificial Intelligence, and advanced telecommunications ditions and operational context.
like 4G and 5G, which profoundly influence the trans
Deep Learning (DL), another ML subset, leverages Ar- port sector. These innovations enable the gathering of tificial Neural Networks (ANNs) to process complex data vast data from diverse onboard devices and equipment patterns and make accurate predictions. DL, particu- installed on train vehicles and along railway tracks. larly through Convolutional Neural Networks (CNNs), is revolutionizing industrial image analysis by extracting meaningful features from images for quality control and
This wealth of data, acquired through smart sensors and relayed to diagnostic systems either onboard or in control rooms, holds immense potential. By employdefect detection. Additionally, DL, combined with Nat- ing appropriate techniques, it can unveil patterns of ural Language Processing (NLP), is streamlining tasks like document analysis and inventory management. The degradation in components and anticipate failures in a timely manner, facilitating optimal maintenance deversatility of AI underscores its pivotal role in the manu- cisions. Traditionally, players in the railway transport facturing industry, driving eficiency gains and fostering innovation.
In this paper, we showcase AI projects in the industrial sector from the University of Naples Federico II node of sector have relied on planned maintenance, often resulting in unnecessary actions and inflated operating costs.
tenance (CBM) ofers a proactive alternative. CBM, an extension of planned maintenance, assesses equipment conditions through direct measurements, enabling timely repairs or replacements when specific conditions are met.
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License leveraging monitoring data and efective predictive techAttribution 4.0 International (CC BY 4.0). niques to anticipate fault occurrences. This approach Mann-Kendall) and correlation, to uncover relationships enables companies to schedule maintenance operations between dataset features and failure patterns. Subseprecisely when needed, leading to cost reductions, de- quently, LSTM networks are utilized to both predict and creased mean time to failures, and overall profit enhance- forecast failures, ofering valuable insights to maintement. The key advantage lies in conducting maintenance nance personnel responsible for rolling stock equipment. preemptively, averting prolonged downtime without re- Our methodology achieves an accuracy exceeding 99% sorting to premature interventions, thus minimizing the for both prediction and forecasting tasks, surpassing exunavailability of rolling stock and infrastructure equip- isting ML models and techniques in the predictive mainment. tenance literature. Moreover, the error rates for pre
Machine Learning (ML) algorithms emerge as power- diction and forecasting tasks are notably low, with a ful tools in maintenance across diverse domains, owing false alarm rate of approximately 0.4% and a mean absoto their support for predictive techniques. ML techniques lute error on the order of 10−4. These results are highly have been applied extensively, from predicting light bulb promising compared to previous studies. Additionally, failures to early detection of machine failures, rotating the methodology is validated against real-world systems machinery failure prediction, and estimating the Remain- by our industry partner, confirming the soundness of our ing Useful Life (RUL) of various assets like hard disks assumptions and approaches. and wind turbines. In the railway domain, ML is gaining prominence in enhancing operations and reliability.
However, the eficacy of ML algorithms hinges on select- 3. Forecast and Anomaly Detection ing the appropriate technique, especially considering the in Photovoltaic System gradual deterioration or sudden failure characteristic of rail systems. Thus, ML approaches for predictive main- In the context of the global energy landscape, the Internatenance must account for such data dynamics to ensure tional Energy Agency (IEA) highlights the pivotal role of accurate failure prediction and forecasting. photovoltaic (PV) energy in driving the ongoing energy
We introduced a deep learning-based methodology transition, as indicated in the World Energy Outlook 2023
which enables the prediction and forecasting of failures, [
The proposed approach analyzes data collected from frequently coalescing into energy communities, aim to numerous sensors distributed across the various subsys- foster sustainable energy exchange paradigms, necessitems of a railway vehicle, with a particular focus on the tating the development of novel tools. These tools must critical train traction converter cooling subsystem. Oper- render photovoltaic production economically viable for ational data from a train fleet spanning several months energy trading while ensuring environmental sustainabilis examined. The framework employs classic statistical ity through reductions in energy storage requirements data analysis techniques, such as trend estimation (e.g., and efective planning for grid load and dispersion/utiarchitecture.
This result allowed us to test an anomaly detection procedure by comparing the predicted result with the power produced by each individual inverter. When considering a single plant and computing the prediction error, it is possible to put into practice a simple but efective anomaly detection technique as shown in Figure 4.
The proposed approach, as presented in Figure 3,
entails the integration of a selected provider from among
global numerical and commercial models. This choice
is informed by preliminary results. The integration in- We are currently immersed in the Industry 4.0 era,
volves coupling this provider with a mathematical and marked by the continual automation of traditional
manuphysical model of irradiance [
Preliminary results shows up to 0.536 ± 0.015% mean given their significant impact on industrial production absolute percentage error (MAPE) on power yield fore- and service availability. Industries are investing heavily casting (99% CI validated) using a seq2seq Transformer in equipping themselves with the tools for data-driven connected network module. maintenance strategies. The experimental results demonstrate how the pro
In the literature, two main approaches support main- posed approach efectively meets the demands of
modtenance: model-driven and data-driven methods, with ern embedded AI applications, particularly benefiting
hybrid-driven approaches gaining traction. While model- smart manufacturing systems where reliability, low
ladriven techniques rely on expert theoretical understand- tency, privacy, and low power are critical. These findings
ing, data-driven techniques leverage the vast information have significant management implications for optimizing
available to detect machinery anomalies ([
In many domains with complex data, machine
learning and deep learning techniques stand out for predictive 5. Forecasting Remaining Useful
maintenance ([
However, deploying deep learning in real-world IoT dards, regulatory requirements, and the importance of
efscenarios faces challenges due to computational limi- ficient maintenance to keep operations running smoothly.
tations. Edge/fog computing solutions are favored but While traditional maintenance methods like preventive
influenced by network connectivity ([
This proposal presents a deep learning approach for these challenges. predictive maintenance, leveraging a multi-head attention PdM uses data from sensors, operation logs, and mainmechanism for high RUL estimation accuracy and low tenance records to monitor equipment health and predict memory requirements, suitable for hardware implemen- failures. By forecasting the remaining useful life of key tation. Experimental results demonstrate its efectiveness components, it prevents unplanned downtime, cuts mainand eficiency, making it a promising solution for real- tenance costs, and improves safety. world PdM scenarios. The focus is on using advanced algorithms such as
Figure 5 presents a high-level overview of the proposed LSTM networks and Transformer models to improve model architecture for the described PdM task, along maintenance schedules in the aerospace industry using with the data analysis pipeline necessary for generating the C-MAPSS dataset, which contains sensor readings estimated RUL values. and remaining useful life (RUL) values for turbofan jet en
The input comprises historical data from sensors pro- gines under diferent operating conditions. These types viding crucial information about the monitored machin- of deep learning models have shown great success in ery’s conditions, which includes a temporal component understanding intricate time-based patterns and distant crucial for detecting degradation trends. connections in data sequences.
Once the input data is processed, it’s fed into the model The C-MAPSS dataset is highly valued in the research capable of capturing temporal dependencies between fea- community and widely used. Many researchers consider tures. By setting an appropriate time window, the input it a valuable resource for studying intelligent maintedata fed into the model forms a matrix of size ( , ), nance and machine health prognosis as shown in the where represents the length of the input time window ifgure 6. and denotes the number of considered features. The The LSTM network, with its gating mechanisms (formodel output is a real number representing the remain- get, input, and output gates) and capability to selectively ing useful life of the machinery. The main components of retain or discard information over time, proved adept the proposed architecture include: positional encoding at modeling long-term dependencies intrinsic to timeblock, accounting for the relative or absolute position of series data. Conversely, the Transformer model, origthe time-steps in the input sequence; the attention mod- inally designed for natural language processing tasks, ule, comprising two sub-layers with residual connections employed a self-attention mechanism and position-wise between them: the multi-head attention block and fully feed-forward networks to capture distant relationships within the input sequences efectively.
Through rigorous testing and analysis using various metrics like mean absolute error, loss function and 2 values, our findings show that LSTM networks outperform Transformers in accurately forecasting RUL 1.
The results analysis indicates that while the Transformer model shows good performance in predicting engine performance, it falls short compared to the LSTM model, likely due to dataset limitations rather than architectural constraints. The Transformer’s self-attention mechanism and lack of an explicit recurrent structure like LSTMs may require more diverse data to accurately capture sequential patterns, especially in complex timeseries tasks. Understanding and predicting engine performance over time relies heavily on modeling long-term temporal dependencies, which can be challenging with a limited number of representative examples in the dataset.
The Transformer’s ability to efectively handle such temporal contexts is influenced by the quality and comprehensiveness of the training data it receives. Further research could explore new model architectures, ensemble techniques and optimization strategies to improve model performance and address specific challenges associated with time series data.