In condition monitoring, early detection of process signal drifts indicating, e.g., equipment degradation is crucial. exponentially weighted moving average (EWMA), cumulative sum (CUSUM), and discrete average block (DAB)-based drift detectors are statistical and commonly used methods. Each has benefits and limitations, suited to different data types. However, EWMA and CUSUM are fixed mean drift detectors, limiting their applicability and adaptability. This article explores adding dynamic behavior to drift detection methods. We use a wide range of synthetic data based on a real-world manufacturing process. The investigated parameter space includes standard deviation, drift rates, and outliers. Besides, each algorithm has some tuning parameters that define its behavior. Two metrics validate experiments against labeled data. Based on our observations, EWMA performs better for drift detection on average, but CUSUM is superior in detecting very small drifts. Furthermore, we derive guidelines for the choice and application of drift detection in practice.

Detecting damages and anomalies on railroads is a tedious and expensive task. This paper proposes the Vision-based Anomaly Detection Algorithm for Railroads (VADAR), which can find rail damages and foreign objects on the trackbed in monochrome images captured by a train-mounted camera system. VADAR analyzes the input image with three Autoencoders (AEs), a segmentation network, and a one-class classifier. The detection of unknown anomalies justifies our architecture's advantage, i.e., no anomalies are necessary for training VADAR. In experiments with a dataset of over 218,000 images, VADAR achieves a detection accuracy of 95% and a recall rate of 70% for smaller and up to 100% for bigger instances of several anomaly classes. Compared with a state-of-the-art approach which is based on more expensive equipment, VADAR achieves accuracy and recall rates (for anomalies of particular interest) of about 22pps and up to 45pps higher, respectively. With a setting that achieves 83.5% accuracy, VADAR's recall rate outperforms the state-of-the-art approach for every anomaly class and object size.

Quantization of Deep Neural Networks is a central technique to reduce the computation load in embedded devices. Even in quantized Deep Neural Networks (DNNs), the scaler/rescaler following a convolution or dense layer often requires a high bit width multiplication and a shift. Previous work has proposed to remove the multiplier by restricting the quantization method. We propose a Quantisation Aware Training (QAT) approach, which explicitly models the rescaler during training, eliminating the limitations of quantization functions and achieving a 30–35% improvement in training time and a significant reduction in memory requirements compared to the state-of-the-art. GitHub: https://github.com/embedded-machine-learning/FastQATforPOTRescaler

This paper introduces a novel methodology for assessing the energy efficiency of neural network accelerators at both layer and network granularity. The approach involves extracting per-layer timing reports from recorded power profiles. The power and energy consumption of three prominent neural network accelerators, namely the Intel Neural Compute Stick 2, the Coral Edge TPU, and the NXP i.MX8M Plus is evaluated for three different Deep Neural Networks (DNNs) using this method. The study investigates the relationship between decreasing sampling frequencies and the average error, as well as the detailed energy consumption of individual DNN layers and layer types. The findings reveal that latency outperforms the number of operations per layer as a predictor for both overall and dynamic energy, with errors of 10 % and 100 % respectively. The main conclusions are: a sampling frequency of 200 kHz is necessary to achieve an average error of 5 %; the number of operations is an inadequate predictor of energy consumption; and specific hardware settings significantly influence power and energy consumption, emphasizing the need for their consideration in estimation.

Abstract Cardiovascular diseases are one of the world’s major causes of loss of life. The vital signs of a patient can indicate this up to 24 hours before such an incident happens. Healthcare professionals use Early Warning Score (EWS) as a common tool in healthcare facilities to indicate the health status of a patient. However, the chance of survival of an outpatient could be increased if a mobile EWS system would monitor them during their daily activities to be able to alert in case of danger. Because of limited healthcare professional supervision of this health condition assessment, a mobile EWS system needs to have an acceptable level of reliability - even if errors occur in the monitoring setup such as noisy signals and detached sensors. In earlier works, a data reliability validation technique has been presented that gives information about the trustfulness of the calculated EWS. In this paper, we propose an EWS system enhanced with the self-aware property confidence, which is based on fuzzy logic. In our experiments, we demonstrate that - under adverse monitoring circumstances (such as noisy signals, detached sensors, and non-nominal monitoring conditions) - our proposed Self-Aware Early Warning Score (SA-EWS) system provides a more reliable EWS than an EWS system without self-aware properties.

Monitoring is an important aspect of operation and maintenance in virtually every industrial system. However, the extent and methods of monitoring vastly vary in different systems, from fully automated to fully manual. One of the challenges of automated monitoring is the tediousness of, and the extent of engineering time and effort required to develop necessary models or machine learning algorithms for the units to be monitored. Model-free monitoring, on the other hand, can save resources and efforts substantially. However, more often than not they have a very limited scope and application. Such a system is needed, for example, to monitor entire Heating, Ventilation and Air Conditioning (HVAC) systems, consisting of different types of sensors such as temperature, pressure, humidity or flow sensors. Recently, we proposed the Context-Aware Health Monitoring (CAH) system for model-free monitoring of any injective-function black-box, and it was tested successfully on an AC motor. In this paper, we evaluate the CAH system for an entirely different industrial use-case, that is, a hydraulic circuit. The results show the potential for considerable benefits in monitoring HVAC systems. Moreover, in the light of applying CAH to different use-cases which may potentially need a different setup of parameters, we performed a sensitivity analysis on the values of different parameters in the system. The results show the robustness of CAH with regard to the values of these parameters.

In healthcare, effective monitoring of patients plays a key role in detecting health deterioration early enough. Many signs of deterioration exist as early as 24 hours prior having a serious impact on the health of a person. As hospitalization times have to be minimized, in-home or remote early warning systems can fill the gap by allowing in-home care while having the potentially problematic conditions and their signs under surveillance and control. This work presents a remote monitoring and diagnostic system that provides a holistic perspective of patients and their health conditions. We discuss how the concept of self-awareness can be used in various parts of the system such as information collection through wearable sensors, confidence assessment of the sensory data, the knowledge base of the patient's health situation, and automation of reasoning about the health situation. Our approach to self-awareness provides (i) situation awareness to consider the impact of variations such as sleeping, walking, running, and resting, (ii) system personalization by reflecting parameters such as age, body mass index, and gender, and (iii) the attention property of self-awareness to improve the energy efficiency and dependability of the system via adjusting the priorities of the sensory data collection. We evaluate the proposed method using a full system demonstration.