Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation

Narwariya, Jyoti; Malhotra, Pankaj; TV, Vishnu; Vig, Lovekesh; Shroff, Gautam

doi:10.48550/arxiv.2006.16556

Cited by 9 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most commonly used inductive bias is in the design of the neural network architecture motivated by the structure of the problem. Recent examples of this include using graph neural networks (Wang et al 2018;Narwariya et al 2020) and modular networks (Andreas et al 2016). Recently, using structural biases in deep neural networks motivated by the nature of bias and the structure of the problem have been successfully evaluated for time series forecasting (Bansal et al 2021).…”

Section: Related Workmentioning

confidence: 99%

Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs

Narwariya¹,

Verma²,

Malhotra³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

In electricity markets, electricity retailers or brokers want to maximize profits by allocating tariff profiles to endconsumers. One of the objectives of such demand response management is to incentivize the consumers to adjust their consumption so that the overall electricity procurement in the wholesale markets is minimized, e.g. it is desirable that consumers consume less during peak hours when cost of procurement for brokers from wholesale markets are high. We consider a greedy solution to maximize the overall profit for brokers by optimal tariff profile allocation, i.e. allocate that tariff profile to a consumer that maximizes the profit w.r.t. that consumer. This in-turn requires forecasting electricity consumption for each user for all tariff profiles. This forecasting problem is challenging compared to standard forecasting problems due to following reasons: i. the number of possible combinations of hourly tariffs is high and retailers may not have considered all combinations in the past resulting in a biased set of tariff profiles tried in the past, i.e. retailer may want to consider new tariff profiles that may achieve better profits, ii. the profiles allocated in the past to each user is typically based on certain policy, i.e. tariff profile allocation for historical electricity consumption data is biased. These reasons violate the standard i.i.d. assumptions as there is a need to evaluate new tariff profiles on existing customers and historical data is biased by the policies used in the past for tariff allocation. In this work, we consider several scenarios for forecasting and optimization under these conditions. We leverage the underlying structure of how consumers respond to variable tariff rates by comparing tariffs across hours and shifting loads, and propose suitable inductive biases in the design of deep neural network based architectures for forecasting under such scenarios. More specifically, we leverage attention mechanisms and permutation equivariant networks that allow desirable processing of tariff profiles to learn tariff representations that are insensitive to the biases in the data and still representative of the task. Through extensive empirical evaluation using the PowerTAC simulator, we show that the proposed approach significantly improves upon standard baselines that tend to overfit to the historical tariff profiles.

show abstract

Section: Related Workmentioning

confidence: 99%

Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs

Narwariya¹,

Verma²,

Malhotra³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, in a multi-modal sensor network (e.g., lighting, environment), each sensor can be represented as a node in a graph, and their latent interconnections need to be learned by using data-driven approaches. Established works [19,25,30,40,42,56,58,71,160,161,175,201,207,208,239,258,284,286,290,318,320,321,334] illustrated the performance of applied GNNs in smart city applications that involved IoT sensor interconnections. Table 3 summarizes the sensor infrastructures, GNN models, and learning targets in the collected works.…”

Section: Iot Sensor Interconnection (Isi)mentioning

confidence: 99%

Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including smart wearables, cameras, smartwatches, and autonomous vehicles can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source code from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Neural networks.

show abstract

“…GRU-CM [11] and NerveNet [34] use the inherent ability of GNNs to generalize well on unseen combinations of test instances that are different from training instances. In GNMR [25], GNNs leverage the knowledge of readily available graph structure to process multi-sensor time series data for the Remaining Useful Life (RUL) estimation in equipment health monitoring. However, these approaches neither take into account data-access restrictions across tasks nor variability in target spaces.…”

Section: A Handling Variable-dimensional Time-seriesmentioning

confidence: 99%

Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions

Gupta¹,

Narwariya²,

Malhotra³

et al. 2021

2021 IEEE International Conference on Data Mining (ICDM)

Self Cite

View full text Add to dashboard Cite

We consider a sequence of related multivariate time series learning tasks, such as predicting failures for different instances of a machine from time series of multi-sensor data, or activity recognition tasks over different individuals from multiple wearable sensors. We focus on two under-explored practical challenges arising in such settings: (i) Each task may have a different subset of sensors, i.e., providing different partial observations of the underlying 'system'. This restriction can be due to different manufacturers in the former case, and people wearing more or less measurement devices in the latter (ii) We are not allowed to store or re-access data from a task once it has been observed at the task level. This may be due to privacy considerations in the case of people, or legal restrictions placed by machine owners. Nevertheless, we would like to (a) improve performance on subsequent tasks using experience from completed tasks as well as (b) continue to perform better on past tasks, e.g., update the model and improve predictions on even the first machine after learning from subsequently observed ones. We note that existing continual learning methods do not take into account variability in input dimensions arising due to different subsets of sensors being available across tasks, and struggle to adapt to such variable input dimensions (VID) tasks. In this work, we address this shortcoming of existing methods. To this end, we learn task-specific generative models and classifiers, and use these to augment data for target tasks. Since the input dimensions across tasks vary, we propose a novel conditioning module based on graph neural networks to aid a standard recurrent neural network. We evaluate the efficacy of the proposed approach on three publicly available datasets corresponding to two activity recognition tasks (classification) and one prognostics task (regression). We demonstrate that it is possible to significantly enhance the performance on future and previous tasks while learning continuously from VID tasks without storing data.

show abstract

Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation

Cited by 9 publications

References 14 publications

Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs

Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs

Graph Neural Networks in IoT: A Survey

Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions

Contact Info

Product

Resources

About