=Paper=
{{Paper
|id=Vol-3762/568
|storemode=property
|title=AI in Industry: Activities of the CINI-AIIS Lab at University of Naples Federico II
|pdfUrl=https://ceur-ws.org/Vol-3762/568.pdf
|volume=Vol-3762
|authors=Alessandro Del Prete,Sofia Dutto,Antonino Ferraro,Antonio Galli,Vincenzo Moscato,Gabriele Piantadosi,Carlo Sansone,Giancarlo Sperlì
|dblpUrl=https://dblp.org/rec/conf/ital-ia/PreteDFGMPSS24
}}
==AI in Industry: Activities of the CINI-AIIS Lab at University of Naples Federico II==
https://ceur-ws.org/Vol-3762/568.pdf
AI in Industry: Activities of the CINI-AIIS Lab at University
of Naples Federico II
Alessandro Del Prete1 , Sofia Dutto1 , Antonino Ferraro1 , Antonio Galli1,∗ , Vincenzo Moscato1 ,
Gabriele Piantadosi2 , Carlo Sansone1 and Giancarlo Sperlì1
1
University of Naples Federico II, via Claudio 21, Naples, 80125, Italy
2
ENEA, Centro Ricerche Portici, 80055 Portici (NA), Italy
Abstract
Artificial intelligence (AI) is reshaping the manufacturing landscape, offering opportunities for efficiency 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.
Keywords
Predictive Maintenance, Energy Forecasting, Anomaly Detection, Remaining Useful Life.
1. Introduction the CINI-AIIS Lab, highlighting their innovative contri-
butions.
Artificial intelligence (AI) is transforming various indus-
tries, including the manufacturing sector, by emulating
human intelligence to tackle complex challenges. In the 2. Prediction and Forecasting for
industrial domain, AI holds significant promise, enhanc- railway rolling stock equipment
ing operational efficiency, optimizing processes, and driv-
ing innovation. AI-powered systems can analyze exten- The manufacturing industry is currently undergoing the
sive datasets to detect anomalies, predict equipment fail- so-called Industry 4.0 revolution, characterized by the ex-
ures, 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 production settings. Key technologies driving this revo-
data and make informed decisions, such as predicting lution 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 This wealth of data, acquired through smart sensors
revolutionizing industrial image analysis by extracting and relayed to diagnostic systems either onboard or in
meaningful features from images for quality control and control rooms, holds immense potential. By employ-
defect detection. Additionally, DL, combined with Nat- ing appropriate techniques, it can unveil patterns of
ural Language Processing (NLP), is streamlining tasks degradation in components and anticipate failures in
like document analysis and inventory management. The a timely manner, facilitating optimal maintenance de-
versatility of AI underscores its pivotal role in the manu- cisions. Traditionally, players in the railway transport
facturing industry, driving efficiency gains and fostering sector have relied on planned maintenance, often result-
innovation. ing in unnecessary actions and inflated operating costs.
In this paper, we showcase AI projects in the industrial However, the evolution towards Condition-Based Main-
sector from the University of Naples Federico II node of tenance (CBM) offers a proactive alternative. CBM, an
extension of planned maintenance, assesses equipment
Ital-IA 2024: 4th National Conference on Artificial Intelligence, orga-
conditions through direct measurements, enabling timely
nized by CINI, May 29-30, 2024, Naples, Italy
∗
Corresponding author. repairs or replacements when specific conditions are met.
Envelope-Open antonio.galli@unina.it (A. Galli) Predictive Maintenance takes CBM a step further by
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0). leveraging monitoring data and effective predictive tech-
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Figure 1: The proposed failure prediction methodology.
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. Subse-
precisely 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, offering valuable insights to mainte-
ment. 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 ex-
unavailability of rolling stock and infrastructure equip- isting ML models and techniques in the predictive main-
ment. 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 abso-
to 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 gain-
ing prominence in enhancing operations and reliability.
However, the efficacy 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 Interna-
tenance 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, [1]. Despite the adoption of the Stated Policies Scenario
allowing for proactive maintenance interventions to be (STEPS), it is observed that the utilization rate of solar
planned before they occur, thereby optimizing both the energy markets lags behind the expanding production
cost and duration of maintenance activities. Utilizing capacity of PV technologies (Figure 2).
Long Short-Term Memory (LSTM) networks, an exten- Recognizing the imperative for a paradigm shift within
sion of recurrent neural networks (RNN), the methodol- the PV market, encompassing both domestic and indus-
ogy is adept at learning long-term dependencies in data trial installations, there is a pressing need for active in-
that change gradually over time. An overview of the volvement from network and infrastructure stakehold-
methodology is provided in Figure 1. ers, energy producers, and consumers [2]. These entities,
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, necessi-
tems 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 sustainabil-
is examined. The framework employs classic statistical ity through reductions in energy storage requirements
data analysis techniques, such as trend estimation (e.g., and effective planning for grid load and dispersion/uti-
architecture.
This result allowed us to test an anomaly detection pro-
cedure by comparing the predicted result with the power
produced by each individual inverter. When consider-
ing a single plant and computing the prediction error,
it is possible to put into practice a simple but effective
anomaly detection technique as shown in Figure 4.
Figure 2: Global solar module manufacturing and solar PV
capacity additions in the STEPS, 2010-2030 [1].
lization.
Figure 4: A single day of PV Power production (scaled to
Inverter Nominal Power) as continuous lines and the relative
model predictions as dotted lines. Highlighted a single faulty
inverter showing a transient power loss. The bar plot of the
model prediction error for each inverter allows to easily rec-
ognize the faulty one by applying a threshold.
Figure 4 shows that an individual inverter has suffered
a transient power loss that can be appreciated visually
by comparing the predicted vs. produced power curves,
Figure 3: The proposed approach for Power Forecasting. The
training dataset is composed by weather data, irradiance but also quantitatively by thresholding the prediction
model and PV configuration (red-dashed PV Pant Data). Tar- error. It is not yet possible to quantify an unambiguous
get data is composed by real daily AC Power Production and threshold, and future work is investigating the possibility
Solar Irradiance (bottom left graph). of a local adaptive approach.
The proposed approach, as presented in Figure 3, en- 4. Predictive Maintenance in IoT
tails the integration of a selected provider from among scenarios
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 manu-
physical model of irradiance [3], with the objective of facturing and industrial processes through modern smart
effectively propagating irradiance contributions through technologies like Internet of Things (IoT) and Artificial
suitable machine learning models. Long short-term mem- Intelligence (AI) ([4]). This evolution demands an increas-
ory (LSTM) and Transformer Neural Networks (NN) mod- ing integration between physical and digital systems in
els will be considered, with comparisons drawn against production environments, enabling the collection of vast
classical machine learning techniques. To optimize the data from various smart equipment and sensors.
model’s performance, an appropriate weighted combina- Smart sensors, devices generating data on physical pa-
tion of losses will be employed during the training pro- rameters (e.g., temperature, humidity, or vibration speed),
cess. The training is conducted using real data sourced offer functionalities ranging from self-monitoring to man-
from managed photovoltaic systems situated at five dis- aging complex processes ([5]).
tinct Italian sites. By leveraging this combination of Analyzing such data yields insights into machinery
numerical, commercial, and machine learning models, health and production levels, driving strategic decision-
the proposal aims to enhance the accuracy and predic- making for benefits like reduced maintenance costs,
tive capability of the system, paving the way for a more fewer machine faults, optimized inventory, and increased
effective utilization of solar energy resources. production. Maintenance procedures are a key focus,
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 effectively meets the demands of mod-
tenance: 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 la-
driven 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 ([6, 7]). Hybrid- production line operations.
driven solutions merge model and data fusion ([8, 9]). Future research could explore further applications of
Maintenance management approaches, as categorized the attention mechanism in predictive maintenance. Ad-
by ([10]), include Run-to-Failure (R2F), Preventive Main- ditionally, investigating what aspects the model priori-
tenance (PvM), and Predictive Maintenance (PdM). Our tizes (i.e., receives more attention) could be insightful,
focus is on PdM, which relies on data-driven analysis potentially leveraging eXplainable Artificial Intelligence
to predict machinery failures, optimizing maintenance (XAI) tools to provide explanations.
procedures and increasing machine longevity ([11, 12]).
In many domains with complex data, machine learn-
ing and deep learning techniques stand out for predictive 5. Forecasting Remaining Useful
maintenance ([13, 14, 15]). These approaches use histori- Life in Aerospace Maintenance
cal datasets to train models for predicting failures, such
as Remaining Useful Life (RUL) estimation. The aerospace industry is known for its strict safety stan-
However, deploying deep learning in real-world IoT dards, regulatory requirements, and the importance of ef-
scenarios 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 ([16]). Embedded AI and reactive maintenance have their drawbacks in terms
techniques are increasingly proposed for efficient, cost- of cost and accurately predicting failures, predictive main-
effective data-driven analysis on industrial equipment tenance driven by AI and data analytics is a more proac-
hardware ([17]). tive and cost-effective solution that can help overcome
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 main-
mechanism 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 effectiveness components, it prevents unplanned downtime, cuts main-
and efficiency, 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 different 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 mainte-
data fed into the model forms a matrix of size (𝑇𝑤 , 𝑁𝑥 ), nance and machine health prognosis as shown in the
where 𝑇𝑤 represents the length of the input time window figure 6.
and 𝑁𝑥 denotes the number of considered features. The The LSTM network, with its gating mechanisms (for-
model 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 time-
block, accounting for the relative or absolute position of series data. Conversely, the Transformer model, orig-
the 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
Figure 5: Proposed AI architecture.
performance and address specific challenges associated
with time series data.
Acknowledgments
This work was supported in part by the Piano Nazionale
Ripresa Resilienza (PNRR) Ministero dell’Università e
Figure 6: Datasets used for RUL prediction della Ricerca (MUR) Project under Grant PE0000013-FAIR
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