Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

A multi-sensor based online tool condition monitoring system for milling process

Tool condition monitoring has been considered as one of the key enabling technologies for manufacturing optimization. Due to the high cost and limited system openness, the relevant developed systems have not been widely adopted by industries, especially Small and Medium-sized Enterprises. In this research, a cost-effective, wireless communication enabled, multi-sensor based tool condition monitoring system has been developed. Various sensor data, such as vibration, cutting force and power data, as well as actual machining parameters, have been collected to support efficient tool condition monitoring and life estimation. The effectiveness of the developed system has been validated via machining cases. The system can be extended to wide manufacturing applications

Multispectral Palmprint Encoding and Recognition

Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

Heterogeneous multi-modal sensor fusion with hybrid attention for exercise recognition.

Exercise adherence is a key component of digital behaviour change interventions for the self-management of musculoskeletal pain. Automated monitoring of exercise adherence requires sensors that can capture patients performing exercises and Machine Learning (ML) algorithms that can recognise exercises. In contrast to ambulatory activities that are recognisable with a wrist accelerometer data; exercises require multiple sensor modalities because of the complexity of movements and the settings involved. Exercise Recognition (ExR) pose many challenges to ML researchers due to the heterogeneity of the sensor modalities (e.g. image/video streams, wearables, pressure mats). We recently published MEx, a benchmark dataset for ExR, to promote the study of new and transferable HAR methods to improve ExR and benchmarked the state-of-the-art ML algorithms on 4 modalities. The results highlighted the need for fusion methods that unite the individual strengths of modalities. In this paper we explore fusion methods with focus on attention and propose a novel multi-modal hybrid attention fusion architecture mHAF for ExR. We achieve the best performance of 96.24% (F1-measure) with a modality combination of a pressure mat, a depth camera and an accelerometer on the thigh. mHAF significantly outperforms multiple baselines and the contribution of model components are verified with an ablation study. The benefits of attention fusion are clearly demonstrated by visualising attention weights; showing how mHAF learns feature importance and modality combinations suited for different exercise classes. We highlight the importance of improving deployability and minimising obtrusiveness by exploring the best performing 2 and 3 modality combinations

Personalised exercise recognition towards improved self-management of musculoskeletal disorders.

Musculoskeletal Disorders (MSD) have been the primary contributor to the global disease burden, with increased years lived with disability. Such chronic conditions require self-management, typically in the form of maintaining an active lifestyle while adhering to prescribed exercises. Today, exercise monitoring in fitness applications wholly relies on user input. Effective digital intervention for self-managing MSD should be capable of monitoring, recognising and assessing performance quality of exercises in real-time. Exercise Recognition (ExRec) is the machine learning problem that investigates the automation of exercise monitoring. Multiple challenges arise when implementing high performing ExRec algorithms for a wide range of exercises performed by people from different demographics. In this thesis, we explore three personalisation challenges. Different sensor combinations can be used to capture exercises, to improve usability and deployability in restricted settings. Accordingly, a recognition algorithm should be adaptable to different sensor combinations. To address this challenge, we investigate the best feature learners for individual sensors, and effective fusion methods that minimise the need for data and very deep architectures. We implement a modular hybrid attention fusion architecture that emphasises significant features and understates noisy features from multiple sensors for each exercise. Persons perform exercises differently when not supervised; they incorporate personal rhythms and nuances. Accordingly, a recognition algorithm should be able to adapt to different persons. To address the personalised recognition challenge, we investigate how to adapt learned models to new, unseen persons. Key to achieving effective personalisation is the ability to personalise with few data instances. Accordingly, we bring together personalisation methods and advances in meta-learning to introduce personalised meta-learning methodology. The resulting personalised meta-learners are learning to adapt to new end-users with only few data instances. It is infeasible to design algorithms to recognise all expected exercises a physiotherapist would prescribe. Accordingly, the ability to integrate new exercises after deployment is another challenge in ExRec. The challenge of adapting to unseen exercises is known as open-ended recognition. We extend the personalised meta-learning methodology to the open-ended domain, such that an end-user can introduce a new exercise to the model with only a few data instances. Finally, we address the lack of publicly available data and collaborate with health science researchers to curate a heterogeneous multi-modal physiotherapy exercise dataset, MEx. We conduct comprehensive evaluations of the proposed methods using MEx to demonstrate that our methods successfully address the three ExRec challenges. We also show that our contributions are not restricted to the domain of ExRec, but are applicable in a wide range of activity recognition tasks by extending the evaluation to other human activity recognition domains

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version