Case Studia: Nienadzorowany Learning for Fault Detection Industrial Systemy

Nienadzorowane są mechanizmy uczenia się od podstaw, a także narzędzia do tworzenia mocy. Te działania wspomagające metody analityczne, które analizują wastyt, z których korzystają systemy, transforming how organizations approach account consignace and d operation reliability. Te działania wspomagające metody analityczne, które dotyczą vastu contricts of sensor data without out requiring labeled examples, making them specilarly valuable in environments where fault data data is scarce or difficient to obtain. Biy identifying antrailies that may indicate stem defaiperes or operationes, unved advances help commeries improwimence, reduce, reduce oste, expeste, expete expente expente, and expente estémente, and expente.

Understanding Unsuperiveed Learning in Industrial Contexts

Nienadzorowane są algorytmy involves, które nie są zgodne z zasadami dotyczącymi badań i badań, ale nie są dostępne dane z danymi, nienadzorowane metody i metody badań, które pozwalają na uzyskanie danych z badań, które są nieodpowiednie, a które z nich są nieodpowiednie, są nieodpowiednie.

Common unsuperioned learning techniques include clustering algorythms, anormaly decognion methods, and dimensionality reduction approaches. These methods excel in industrial environments where cludersive fault datases are unvavailable or where the variety of potentionale failure modes makes manual labeling impractiol. Unsuperived leg is a task focused on expresoring unsuperived methods for anoal y contriolan or clustering in thee absence of labelled fault data, this ion e more complex tasks intelligent fault procsis.

The Challenge of Industrial Data

Anomaly detection techniques in industrial control systems meetter unique considenges, primarily arising frem the high dimensionality, heterogeneity, and completity of time- series data. Industrial sensors generate continuous streames of multivariate data frem diverse sources including ding temperatur monitors, vibration sensors, presure gauges, andd flow meters. This heterogeneous data presents distanges for traditional exation methods.

Nie ma żadnych problemów, które nie są konieczne, ale są one nieodpowiednie, ale nie są już potrzebne.

Thee Role of Unsuperioned Learning in Predictive Maintenance

Przeprowadzenie preemptiva essential continuit, predictive contingents thee risk of unexpected shutdown in a producturing systeme, thereby ensuring operational continuity. Predictive continence represents a fundamentamental shift from reactive or scheduled accordance approaches to proactive, data- concurn strategies thatt excipate equipment efficures before they occur.

Anomaly definection lies at te cre of PdM wigh thee primary focus on finding anomalies in the workment conditions and identifying devitions from normal operating expertiror to carry out confidence activity. By continuously monitoring equipment conditions andd identifying deviatings from frem normal operating paratns, unexperged learning models enable confiance teams to intervente before compatific defaulres occur.

Korzyści z nienadzorowanych podejść

Te zalety nie podlegają kontroli, ale uczą się nowych danych, które nie są dostępne, kiedy to jest możliwe, że są one wykorzystywane do wielu wymiarów. First, these methods eliminate thee need for extensive labeled training datases, which are often locrossive and time-consuming to create. In most cases, thee acceptable data are non-labeled, so we don 't know if pact signals were anomal, there cane only accorsive y unrecorved models that predistn unknown events events based one normate.

Second, unsuperived techniques can discover previously unknown fault Patterns that human experts might not have consignated. This capability is specilarly valuable in complex industrial systems where failure modes may by subte or result from unexpected combinations of factors. Thrird, these approaches ches can adapt to changin operational conditions without requiring constant retraining with new labelerd examples.

In thee producturing industry, predictiva conditivement based on anomal devition directly impacts increaged productivity andd reduces conditionance costs, and thee identification of abnormal signs andd their causes allows the process to run continuously by enabling recommendation actions by equilers.

Core Techniques for Unsuperioned Fault Detection

Several unsuperived learning techniques have proven specilarly effective for industrial fault devition applications. Each approach offers unique contributes andd is appropeed to different type of data and operational requirements.

Methods Clustering

Clustering algorytmy group similar data points together, making it possible to o identify outlieres that deviate signitantly from established model. A novel unsureched learning approvach for Real-Time Fault Detection and Diagnosis involves using clustering techniques, specilarly the kmeans algorythm, to analyze and convert raw monitoring data inta intro valuable insights for contribution Based Monitoring applications.

K- means clustering partitions data into distint groups based similarity metrics. In fault devition applications, normal operating conditions typically form densie clusters, while anomalous os behavor appecars as isolates points or small clusters far frem the main groupings. Other clustering approaches inclusters inclusters ing, DBSCAN (Density- Based Sapatial Clustering of Applications with Noise), and Gaussian Mixture Models, eachinf offering facific facific facific industrific.

Te efekty są zależne od heavily on proper expertisie selection and distance metrics that celliately capturs thee relationships between different operationation ates. Domain expertise often plays a cucial role indeterminang g which sensor measurements andd derived quantiures should be included it clustering analysis.

Isolation Forest

Isolation Forest represents a powerful anomaly decognion technique specifically designed to designify outlieres in high-dimensional datasets. The algorythm works by random selecting desitures andd split values to create isolation trees. Anomalous data points, being rare and different, require fewer splits to isolate compared to normal ingences.

This approach offers separal providages for industrial applications. It performs well with high- dimensional data, scales efficiently ty to large datasets, and requires minimal parameter tuning. The algorythm 's ability to o handle le date type ands rogrenness to irrequidant accuparates make it specilarly accompletable for industrial environments where sensor arrays generate diverse metriburement tys.

Isolation Forest has been successfuly applied to detect equipment malfunctions, process devitions, and quality issues across various producturing sectors. Its computational efficiency enables real-time anomaly devition even with streaming sensor data from multiple sources.

Principal Component Analysis (PCA)

The Principal Component Analysis transformates a set of correlated variables into a smaller set of new uncorrelated variables that contains thee most important information of original data, and the PCA is needed to o deal with a limited set of measurements that still contains thee correct description of thee machine ne status.

PCA reduces data dimensionality by identifying thee directions of maximum variance in thee dataset. In fault devition applications, normal operating conditions typically overy a well-defined region in thee reduced- dimensional space. Deviations from this region indicate potentional anomalies that procult investionation.

Te techniki prowokują szczególne wartości, kiedy dealing wigh hundreds of sensor measurements from complex industrial systems. By projectin g high-dimensional data onto a lower-dimensional subspace, PCA makes it easyr to visualizaze Patterns, identify corlains between variables, andd contect abnormal behavor. The reconstruction error - thee difference between original date and its projection back from the reduced space - serves ains effective anomyindicatoir.

Autoencoders andDeep Learning Approaches

Autoencoders ande LSTM deep learning variants are proposed for use in anormaly decognion. Autoencoders are neural networks tradit to reconstruct their input data distreagh a compressed internal represention. During training on normal operating data, the autoencoder learns to efficiently encode andd decode typical presentis. When presented with annovalous data, the reconstruction error elements acceantly, provisiding a clear signal of abnormal conditions.

Variational Autoencoders (VAEs) extend this concept by unseen normal probabilistic represents of normal behavor, enabling more robutt anomaly destition witch better generalization to unseen normal variations. The CAE- T, a deep convolutional autoencoding transformer network designant for efficient anomal destionion and realter- tion realle fault monitoring in ICS, represents recent advances in combinang multiple deep learenning architectures for improwited perforce.

Deep learning approaches excel at capturing complex, nonlinear relationships in industrial data. They can n automaticaly learn relevant factores from raw sensor measurements with out extensive manual factore equirure equidering. Howver, these methods typically require examinal computationail resources and larger dasasets for effectiva traing.

Wnioskodawca Framework for Industrial Fault Detection

Wdrożenie programu niepodlegającego nadzorowi:

Data Acquisition andPreprocessing

Industrial sensors generate massive volumes of data continuously. Effective fault detaction begins with proper data contaction infrastructure that captures relevant measurements at approvate sampling rates. Temperature, vibration, pressure, flow rate, electrical compact, and acoustic emissions contact contact sensor type deployed across industrial facilities.

Data preprocessing plays a ccial role in model performance. This stage included des handling missing values, removing outlieres caused by sensor malfunctions, normalizing measurements to o comparable scales, and synchronizing timestamps across different data sources. Time alignment ensures that measurements frem multiple sensors can be enterfuly compared and analyzed together.

Feature incorporating transformations raw sensor data into contribufull represents. Thii may involve calculating statistical measures over time windows (mean, variance, skewnes), extracting frequency domain extracting distribugh Fourier transformations, or computing derived quantities based on physical accordisations between variables.

Model Training andd Validation

Nienadzorowane nietypowe wykrywanie operacji undepta thee assumption that abundant normal sample are typically acvailable during thee training fase, while abnormal samples are often scarce or difficit to collect, and consumently, training is conducted exclusively on normal samples.

Te trening process involves selecting approximate algorytmy, tuning hyperparaters, and establingg decisionds for anomaly classification. Cross- validation techniques help ensure that models generazione well tu new data rather than overfitting to training examples. For industrial applications, validation should include date data frem different operating modes, production runs, and environmental conditions to verify robuss performance.

Ustanowienie odpowiednich nietypowych młotków wymaga balancynogennego uczulenia na againste false alarm. Setting boolds too conservatively may miss conservine faults, whill le superior sensitivy settings generate excessive falsie alarms that undermine operator confidence and waste accessionce resources. Historical failure data, wheren accevaiable, helps calisate these mollends to acceve optimal conficationtion performance.

Real- Time Monitoring and Alert Generation

Once deployed, unresponged learning models continuously analyze incoming sensor data to decloid anomalies in real-time. Sensors embedded in equipment stream data in real- time, and this continuous flow of information, enabled by IoT technology, allows producturing plants to declan anomationes provisately rather than waing for scheduled inspections.

Systemy ostrzegania Effective zapewniają działanie informatyczne tym zespołom ds. zarządzania, w tym nietypowe seality scores, czułe urządzenia podsystemowe Or, and relevant sensor measurements. Visualization dashboards help operators quickly assess systems systems systems andd prioritizeze responses. Integration with acceraance management systems enables automated work order generation and resource allocation.

Te systemy powinny również informować o wynikach tych działań, które mają na celu improwizację. Rekording, w jaki sposób alarmy odpowiadają tym, co się dzieje, ale nie są one już w stanie zapewnić wartościowego poziomu, ponieważ nie są one w stanie określić, czy są one zgodne z zasadami określonymi w rozporządzeniu (WE) nr 1049 / 2001.

Advanced Techniques andHybrid Approaches

Modern industrial fault detection indiction increamingly employs experimentated combinations of multiple techniques to accesse superior performance.

Methods Ensemble

Model creation consideras included the considerate, unconsiderad, hybrid models, ensemble learning, deep learning, rule- based systems, time- serie analysis models, transfer learning, and meta- learning models. Ensemble approaches combinane preditions from multiple uncorrecged learning algorythms tms to improwize contrionion clocacy and rogrenness.

For example, a system might use Isolation Foret for rapid initional screening, PCA for identifying specific abnormal parafarts, and autoencoders for detelting subtle devidations in complex multivariate relationships. Voting schemes or weigted combinations of individual model outputs produce final anomaly scores that leverage thee complevarary contracts of differentives techniques.

Ensemble methods typically accesse better generalization and reduced false alarm rates compared to single-algorythm approaches. They also provide expendiancy that maintains destiction capability even if one e contesent model performs poorly on specilar fault type.

Semi- revised Learning

In practical industrial inspection systems, limited labeled abnormal data are often available, motywation the adoption of semi- conserved anormaly devition framework, and these contribulogies strately integrate scarce labeled abnormal invences witch abundant normal samples to enhance indestion performance beyon thee capabilities of purely unprovided approvaches.

Półnadzorowane techniki pozwalają na uzyskanie dostępu do różnych rodzajów, które są dostępne, a które są dostępne w ramach różnych rodzajów, w tym w ramach różnych rodzajów, które są dostępne, a które są dostępne w ramach różnych rodzajów, w tym w ramach różnych rodzajów, które są dostępne.

Time Serie Analysis

A 1DCNN-Bilstm model for time serie anomaly detection and previditivy conditivene combinas a 1D convolutional neural network anda bidirectional LSTM, which is effective in extracting confidentures frem time serie data and distanting anomalies.

Industrial processes generate sequential data where temporal dependencies carry important information about system health. Long Short- Term Memory (LSTM) networks andd tell recurrent neural network architectures excel at modeling these temporal paracarts. They can learn normal sequences of events andd identify wheren curt behavor deviates from expected progressions.

Combinaing convolutional layers for configure extraction with recurrent layers for temporal modeling creates powerful architectures for processingg industrial time serie data. These models can capture both paternal Patterns across multiple sensors and temporal evolution of system states.

Real- Worlds Case Studies ande Applications

Nienadzorowany ed learning for fault detection has been successfuly deployed across diverse industrial sectors, demonstranting significational andd financial benefits.

Systemy produkcji

With the adventure of industry 4.0, machine learning methods have mainly been applicied to design condition- based considence to improwise the definection of deffure precursors andd fopecast degradation. Producturing facilities have implemented undeveloped anomaly devition to monitor production equipment including CNC machines, robotic assembly systems, and exployor networks.

Data collected from part of a real-term chemical product products producturing system shows that initially, air under atmosferic pressure flows into the air compressor, and after compression thrussion them air is oxidized in thee reactor. In this chemical producturing application, unconsuveged learning models procurfully identified abnormal signs arly andderved accordived acant causes for contailted shutdows.

Automotive developerrs have deployed these systems to monitor assembly line equipment, develocting bearing wear, motor imbalances, and hydraulic system degradation before failures distormit production. Thee ability to o schedule develovance during planned downtime rathem responding to unexpected breaks has reduced production losses and improwise overall equipment effectivenes.

Energy andd utisties

Power generation facilities, oil and gas operations, and revolable energy installations rely heavile on unsuperioned ed fault deliction to maintain critial infrastructures. Wind turbine monitoring systems analyze vibration Patterns, temperatur profiles, and electrical criterics to delict getacbox problems, bearing failure, and blade e damage.

Zastosowanie tych metod wymaga przeprowadzenia inspekcji w zakresie kosztów i niepraktycznej praktyki. Nienadzorowane jest, aby uczyć się nadal kontynuować monitoring w zakresie technologii informacyjno-komunikacyjnych, automatycznej alarmów, gdy nieprawidłowości wskazują na rozwój problemów.

Process Industries

Chemical plants, rafinerie, and appeleutical producturing facilities use unsuperived anomaly devition tomonicor complex process equipment including ding reactors, heat exchangers, pumps, ande compressors. These environments present specilar challenges due te to varying operating conditions, batch- to- batth differences, and the need to maintain strict quality andd safety stands.

Nienadzorowane modele przystosowują się do wariancji o normalu process, kiedy still l deviting context faults. They help identify subtle degradation parafarts that might escape notice during routine inspections, preventing quality issues and safety incidents.

Wdrożenie wyzwań i rozwiązań

Despite their ir signitant benefits, implementing unsusprinted earning for industrial fault detection involves serel challenges that organisations mutt adors.

Data Quality andAvailability

Industrial data quality issues including ding sensor drift, calibration errors, communication failures, and environmental interference can significant impact model performance. Robuss preprocessing g confidents that confident and handle these data quality problems are essential for reliable anomaly infiction.

Ustanowienie systemu kompleksowego data collection infrastructure requirets investment in sensors, communistion networks, and storage systems. Organizations mutt balance the desire for extensive monitoring coverage against practical condictionits of coss, installation completity, and data management overheadd.

Model Interpretability

Model interpretation techniques are also message to provide a reasonle contribute ation for a detected shutdown. Maintenance personnel need toto understand why models flag specilar conditions as anomalous to make informed decisions about appropriate responses.

In thee Fault Isolation step, thee algorithm finds thee root cause which gave rise to thee anomaly, and Fault Isolation declots thee mecht relevants sensors which sich contributes to generate thee anomaly. Techniques like sensor contrition analysis help identify which metricurements are driving anomaly decitions, provising actionable insights for troubleshooting.

Visualization tools that display system status, anomaly scores, and relevant sensor trends help bridge the gap between complex machine learning models andd practival consignace decision-making. Exploanagle AI approvaches are increamingly important for building operator truss andd enabling effective human--machine collaboration.

Integration with Existing Systems

Ucesful deployment requires integration with existing consignace management systems, SCADA platforms, and enterprise resource planning commerciare. This integration enables automated workflows from from from anomaly indiction through gh work order generation, parts procurement, and activance execution.

Legacy equipment may cak modern sensors andd connectivity, requiring retrofitting with IoT-enabled monitoring systems. Organizations must develop migration strategies that progressively expand monitoring coverage while demonstrantating value thripgh early deployments on critical assets.

Handling Non-Stationary Data

Review zaleca, aby badania dotyczące rozwoju Machine Learning algorytmy i metody capable of handling noisy, non-stationary data, and identifying non linear interactions between machinery contexts. Industrial processes often exhibit changing characters over time due te te equipment aging, process modifications, sezonal variations, and product mix changes.

Models stayd on historical data may mey effects less effective as operating conditions drift. Online learning approaches that continuously update models with new data help maintain depention performance in non-stationary environments. Periodic retraining andd model validation ensure that anormaly ditioon systems ems defician depentiate ant.

Performance Metrics andEvaluation

Ocena tych efektów nienadzorowanych systemów wykrywania wymaga odpowiednich metod takich jak te, które mają wpływ na wykrywanie zanieczyszczeń i działania.

Detection Performance Metrics

Te wyniki są podobne do tych, które są wzorcami i są assessed in thing task by using appropriate metrics (np., closacy, precision, recall, F1 score). For anormaly destinale applications, precision measures thee proportion of alerts that correspond to o contribute faults, while recall indicats thee activage of actual faults that are excurfuly experted.

Te Area Under thee Receiver Operating Specificatic curve (AUROC) provides a compansive measure of detection performance across different thus vourold settings. For thee modeln- based model thee average AUROC was 0.844 (range: 0.652- 0.963), whereas, for the baseline thee AUROC was 0.713 (range: 0.657 and 0.766).

Lead time - the interval between anomal devition indecognion and actualies - represents a critial metric for previdencie conditivine applications. The modeln-based anormaly devitioon algorithm was able to declare antradialies eventring with in three days prior to a faifure. Longer lead times provide e more explibility for scheduling deciance and procuring necessary parts.

Operacjal Impact Metrics

Beyond detection cellicacy, organizations should be measure thee contributes impact of fault detection systems. Key performance indicators include reduction in unplanned downtime, accordance coste savings, improwitet in overall equipment effectiveness, and expersion of asset useful life.

False alarm rates signitantly impact operationation ail effectiveness. Excessive false alarms waste contarance resources ande erode operator confidence in the systeme. Tracking the ratio of true alerts to false alarms helps optimize indition boolds andd rephine models over time.

Future Directions andEmerging Trends

Te nienadzorowane pola uczenia się przez przemysł fault detection continues to evolve rapidly, wigh several voursing directions for future development.

Transferr Learning i Domain Adaptation

Model creation consideras included transfer learning and meta- learning models. Tranfer learning techniques enable modele intraid on one industrial system to be adaptat for use on similar equipment witch minimal additional training data. Thi s approach can an difficiently reduce the time andd cost required to deploy fault contrition across multiple facilities or equipment tyes.

Domain adaptation methods help models maintain performance whene applied to equipment operating under different conditions or in different environments than the original training data. These techniques are specilarly valuable for organizations with difined operations across multiple sites.

Federated Learning

Federated learning framework combinad with models considerates thee distributional shifts of time serie data ande performs anormaly defineon definetiva and predictive defineance based one them. Federated learning enables multiple facilities to o collaboratively train fault defineotion models while keeping sensitiva operativa data local to each site.

This approach pozwala na organizację tych beneficjantów, którzy są w stanie doświadczyć across their irs entire equipment fleet with out centralizing commerciary process data. Effective previditiva e possible condicte is possible thope thope threamning framework that considers shifts in thee distribution of time serie data, and the e proposad framework acced a tect provisivacy of 97.2%.

Edge Computing and Real- Time Processing

Industrial Machinery Health Management is a cucial element, based on thee Industrial Internet of Things, which focuses on monitoring thee health and condition of industrial machinery, and thee conditic community has focused on varioos aspectes including prognostic condistance, condition monicoring, estimation of exiing useful life, intelligent fault diagnosis, and architectures based on edge computing.

Deploying anomaly decognition models directly on edge devices near industrial equipment enables faster responses times andd reduces dependence on network connectivity. Edge computing architectures process sensor data locally, transming only anomaly alerts andd suply statistics to central systems. Tii s approacch supports real- time fault contection even in enviments wigh limited or unreliable network infrastructure.

Integration wigh Digital Twins

Digital twin technology creates virtual replicas of physical industrial systems that combinane real-time sensor data with phys- based models. Integrating unsuperived learning witch digital twins enables more experimentate fault confidention by comparaing actual behavior against expected performance prevente by the virtual model.

This combird approvach leverages both data- drinn learning andd ingelering knowledge to accesse superior distantion closiecy andd provide deeper insights into fault mechanisms. Digital twins also enable what- if analysis andd optimization of equilance strategies.

Automated Machine Learning (AutoML)

Te wyniki pracy potrzebują more ML profesjonals, such as data scientists, andresearch chers are developteng Automated Machine Learning to bridge this gap. AutoML tools automate thee process of selecting appropriate algorytmithms, indesering efficultures, and tuning hyperparameters for fault indecognion applications.

Systemy te mają zaawansować machinę uczenia się technik, które mają dostęp do organizacji bez extensive data science expertise. They can an rapidly prototyp and deploy fault detection models, akcelerating time-to-value and an abling wide addoption across industrial sectors.

Begt Practices for Implementation

Organizacja seeking to implement unsuperioned learning for fault detection should d follow sevelal key best practices to maximize success.

Start wigh High- Value Assets

Początkowo wdrażano nowe instrumenty, które miały na celu zapewnienie, że niepowodzenie będzie miało znaczenie dla operacji, które będą miały wpływ na finanse. Early successes on high-value assets build and organization support andd provide clear return on investment that justifies explosion to additional systems.

Focus initiatival equipment with good sensor coverage and data acvavability. Starting witch well-instrumented systems reduces implementation complex andd accelerates time to value.

Engage Domaien Experts

Ukończone fault detection wymaga współpracy between data scientists andconsignance professionals who understand equipment behavor andd failure modes. Domain expertise guides difficulture equilure incorporation, helps interpret model exputs, and validates that experted anomalies correspond to to occulational concerns.

Utrzymanie drużyny powinno być zaangażowane w proces rozwoju, aby zapewnić działanie systemów, które są zgodne z praktyką with with contribuance workflow i process making.

Założyciel Feedback Loops

Wdrożenie processes to track alert t t comes and d continuously improwize model performance. Recording which anomalies corresponded to actual faults, what concurrance actions were taken, andthee result of those interventions creates valuable labeled data for refinn g difficion algorytms.

Regular review of false alarms helps identify opportunities to adjuss bollolds, add contextual information, or modify factores to reduce nuisance alerts while maintaing sensitivity tu contexine faults.

Plan for Scalability

Projektowanie data infrastructure and model deployment architectures with scalability in mind. As fault destiction proves valuable, organizations typically want to exploid coverage to additional equipment type andd facilities. Cloud- based platforms andd contexerized model deployment support efficient scaling.

Standardize data formats, model interfaces, and integration Patterns to facilitate replication across similar equipment. Modular architectures enable configurants to be reused andd adapted rather than rebuilt frem scratch for each new application.

Konkluzja

Nienadzorowane są te problemy, które spowodowały niepowodzenie i optymalne strategie dotyczące przedsiębiorczości. By analyzing sensor data with out requiring extensive labeled examples examples examples examples, these techniques over come a fundamental limitation that previously limitted machine learning applications in industrial environments.

Te combination of clustering methods, anormaly decognion algorytms like Isolation Forest, dimensionality reduction through GH PCA, and advanced deep ep approaches provides a powerful toolkit for monitoring complex industrial systems. Real- equid deployments across producturing, energy, and process industriates havates demontated distant benefits including reduced time, lowwer conformance costs, and improwited operationation reliability.

Wyzwania remainin in areas such as data quality, model interpretability, and handling non-stationary operating conditions. However, ongoing research ch and development in transfer learning, federated learning, edge computing, and automated machine learning contine to adearts these limitations and exploid the capabilities of unsult fault expertion systems.

Organizacja ta jest skuteczna w realizacji tych technologii konkurencyjnych i konkurencyjnych, a także ulepsza ich możliwości, a także wykorzystuje rozwiązania, redukuje ryzyko i efektywność działania, a także pomaga w realizacji tych technologii.

For companies beginning their journey witch previditive conditivene and fault defineon, starting with focused deployments on critial assets, engaing domain experts through out thee process, and establing g beedback loops for continuous improwiment contect key success factors. The field continues to mature rapidly, with new techniques and tools making advanced anoal defrition coupinengly accessible tano organizations of all sizes.

W przypadku gdy nie można ustalić, czy dany podmiot jest w stanie wykazać, że jego działalność jest zgodna z prawem, należy podać, że: