A Sensor Fault-Resilient Framework for Predictive Emission Monitoring Systems

Angelosante, Daniele; Guerriero, Marco; Ciarlo, Gregorio; Bonavita, Nunzio

doi:10.23919/icif.2018.8455445

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…Schneider et al (2017) achieved accurate near real-time air quality monitoring from multiple low-cost sensors using geostatistic-based sensor fusion methodology that merges sensor data and predictions of urban-scale air quality data. ABB has also reported a fault-resilient PEMS framework to identify and reconcile the faulty sensor readings for emission monitoring (Angelosante et al, 2018).…”

Section: Future Directionmentioning

confidence: 99%

A New Area of Utilizing Industrial Internet of Things in Environmental Monitoring

Lou¹,

Mukherjee²,

Wang³

et al. 2022

Front. Chem. Eng.

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Due to environmental regulations continually reducing emission quantity allowed over time, there is a growing need for adaptable and feasible environmental monitoring, such as emission, wastewater quality, and air pollution monitoring, for the process industry (and surrounding communities). Alternative environmental monitoring and process monitoring technologies based on industrial internet of things (IIoT) and artificial intelligence (AI) enable the process industry to take a proactive approach toward the environment and asset integrity management. The monitoring devices can be deployed in a stationary or dynamic manner. In this study, the emerging trend and various applications of IIoT and advanced data analytics methodologies in environmental monitoring are reviewed. An example showing challenges and research needs in sensor placement is given. Future directions in technology, regulation, and application have been discussed as well.

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Section: Future Directionmentioning

confidence: 99%

A New Area of Utilizing Industrial Internet of Things in Environmental Monitoring

Lou¹,

Mukherjee²,

Wang³

et al. 2022

Front. Chem. Eng.

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“…Detect when a PEMS output should be labelled as "bad quality" because one of the related model input is faulty; b) Identify which is the sensor to be blamed as faulty (in order to promptly alert maintenance department); c) Whenever possible, substituting the faulty sensor reading with a reconciled value with the objective of preserving PEMS acceptable performances (Angelosante et al, 2018).…”

Section: Figure 2 Basic Sensor Fault Detection In Present Pemsmentioning

confidence: 99%

“…LWR have been adopted in the final implementation of the algorithm. Although a complete description of the algorithm is outside the scope of this work-the interested reader can refer to (Angelosante et al, 2018) for a more rigorous treatment of the sensor fault isolation, detection and reconciliation problem-in the next section a short introduction to LWR is given.…”

Section: Figure 5 Training Set Example For N=3 Sensorsmentioning

confidence: 99%

Enhanced PEMS Performance and Regulatory Compliance through Machine Learning

Ciarlo¹,

Angelosante²,

Guerriero³

et al. 2018

Self Cite

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Modeling technologies can provide strong support to existing emission management systems, by means of what is known as a Predictive Emission Monitoring System (PEMS). These systems do not measure emissions through any hardware device, but use computer models to predict emission concentrations on the ground of process data (e.g., fuel flow, load) and ambient parameters (e.g., air temperature, relative humidity). They actually represent a relevant application arena for the so-called Inferential Sensor technology which has quickly proved to be invaluable in modern process automation and optimization strategies (Qin et al., 1997; Kadlec et al., 2009). While lots of applications demonstrate that software systems provide accuracy comparable to that of hardware-based Continuous Emission Monitoring Systems (CEMS), virtual analyzers are able to offer additional features and capabilities which are often not properly considered by end-users. Depending on local regulations and constraints, PEMS can be exploited either as primary source of emission monitoring or as a back-up of hardware-based CEMS able to validate analyzers’ readings and extend their service factor. PEMS consistency (and therefore its acceptance from environmental authorities) is directly linked to the accuracy and reliability of each parameter used as input of the models. While environmental authorities are steadily opening to PEMS, it is easy to foresee that major recognition and acceptance will be driven by extending PEMS robustness in front of possible sensor failures. Providing reliable instrument fail-over procedures is the main objective of Sensor Validation (SV) strategies. In this work, the capabilities of a class of machine learning algorithms will be presented, showing the results based on tests performed actual field data gathered at a fluid catalytic cracking unit.

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“…Background, Motivation, State of the Art Modern industrial process plants are large scale, highly complex systems continuously measured and monitored by a large number of sensors to ensure product quality and efficient and safe operations [32]. Accurate fault detection and diagnosis are of the utmost importance to minimize downtime, increase the safety of the plant operations and meet the increasingly stringent safety and environmental regulation requirements [23,72,75]. Any component of an industrial process plant can be susceptible to a fault [16].…”

Section: Introductionmentioning

confidence: 99%

A Scalable Algorithm for Identifying Multiple-Sensor Faults Using Disentangled RNNs

Haldimann¹,

Guerriero

Maret

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

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The problem of detecting and identifying sensor faults is critical for efficient, safe, regulatory-compliant and sustainable operations of modern systems. Their increasing complexity brings new challenges for the Sensor Fault Detection and Isolation (SFD-SFI) tasks. One of the key enablers for any SFD-SFI methods employed in modern complex sensor systems, is the so-called analytical redundancy, which is nothing but building an analytical model of the sensors observations (either derived from first principles or identified from historical data in a data-driven fashion). In a nutshell, SFD amounts to generate and to monitor residuals by comparing the sensor measurements with the model predictions with the idea that the faulty sensors will result in large residuals (i.e. the defective sensors generate measurement that are inconsistent with their expected behavior represented by the model). In this paper we introduce a disentangled Recurrent Neural Network (RNN) with the objective to cope with the smearing-out effect, i.e. the propagation of a sensor fault to the non-faulty sensors resulting in large misleading residuals. Moreover, the introduction of a probabilistic model for the residual generation allows us to develop a novel procedure for the identification of the faulty sensors. The computational complexity of the proposed algorithm is linear in the number of sensors as opposed to the combinatorial nature of the SFI problem. Finally, we empirically verify the performances of the proposed SFD-SFI architecture using a real data set collected at a petrochemical plant.

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A Sensor Fault-Resilient Framework for Predictive Emission Monitoring Systems

Cited by 4 publications

References 18 publications

A New Area of Utilizing Industrial Internet of Things in Environmental Monitoring

A New Area of Utilizing Industrial Internet of Things in Environmental Monitoring

Enhanced PEMS Performance and Regulatory Compliance through Machine Learning

A Scalable Algorithm for Identifying Multiple-Sensor Faults Using Disentangled RNNs

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