Adaptive Alarm Research Articles

A Study of Adaptive Threshold Based on the Reconstruction Model for Marine Systems and Their Equipment Failure Warning

To achieve the failure warning of marine systems and their equipment (MSAE), the threshold is one of the most prominent issues that should be solved first. In this study, a fusion model based on sparse Bayes and probabilistic statistical methods is applied to determine a new and more accurate adaptive alarm threshold. A multistep relevance vector machine (RVM) model is established to realize the parameter reconstruction in which the internal uncertainties caused by the degradation process and the external uncertainty caused by the loading, environment, and disturbances were considered. Then, a varying moving window (VMW) method is employed to determine the window size and achieve continuous data reconstruction. Further, the model based on Johnson distribution systems is utilized to complete the transformation of the residual parameters and calculate the adaptive threshold. Finally, the proposed adaptive decision threshold is successfully involved in the actual examples of the peak pressure and exhaust temperature of marine diesel engines. The results show that the proposed method can realize the continuous health condition monitoring of MSAE, successfully detect abnormal conditions in advance, achieve an early warning of failure, and reserve sufficient time for decision-making to prevent the occurrence of catastrophic disasters.

Adaptive alarm prediction in optical network based on model generalization in cross-layer AI

Artificial intelligence (AI) applications about optical network management are increasing, especially in fault diagnosis. When the network fault or alarm occurs, the equipment running status and performance parameters are changing. AI algorithms can extract the internal relationship from the data and it is proved that the alarm prediction method based on AI is effective. However, the performance of AI models, especially supervised learning, rely on large data sets. These training sets with only a small number of samples are usually unavailable due to the lack of historical data. How to improve the generalization ability of AI models is still a key problem to be solved in the intelligent maintenance of optical networks. In optical network alarm prediction, performance data of devices have different degrees of relationship to alarms and the generalization performance of models is limited by the data structure. In this paper, we propose a method to optimize the model generalization ability in alarm prediction, i.e., the training of a small sample model is solved through parameter transfer in the training process. We verify the generalization effect of the models by using real data of a commercial optical network. Experimental results show that the models achieve significant performance improvement in the data set with a high correlation between performance and alarm.

Fault Diagnosis Method for Wind Power Equipment Based on Hidden Markov Model

The aging of mechanical equipment brings a lot of inconvenience to its application. Therefore, research on fault diagnosis technology of mechanical equipment is of great significance for maintaining equipment safety, improving production efficiency and reliability. Bearings and gears are one of the key components of mechanical equipment, and their working conditions seriously affect the changes in equipment performance. Therefore, the fault diagnosis and performance degradation assessment of bearings and gears have always been the research focus of equipment fault diagnosis. This paper proposes a multichannel information fusion method based on the coupled hidden Markov model, discusses the application of the coupled hidden Markov model in bearing fault diagnosis and performance degradation assessment, and studies the multichannel information fusion method based on the coupled hidden Markov model. Channel monitoring data performance degradation evaluation modeling and performance index calculation method are used, and the calculation method of adaptive alarm limit is given by using the performance index. Finally, the natural failure test data and accelerated fatigue test data of gears and rolling bearings are used to analyze and verify the effectiveness of the coupled hidden Markov model for evaluating the performance degradation of complete and incomplete data. The results prove that the selected performance indicators can be quantified, reflecting the degree of bearing performance degradation.

Stream-data-clustering based adaptive alarm threshold setting approaches for industrial processes with multiple operating conditions

The setting of alarm thresholds is a critical concern of alarm management systems in industrial processes. Conventional alarm thresholds less consider changes of operating conditions in production processes, which degrades the effectiveness of alarm management systems. In response to this problem, this paper proposes an adaptive alarm threshold setting approach based on stream data clustering (SDC). Firstly, we develop a stream data clustering algorithm termed as a-DenStream algorithm which realizes industrial flow data clustering through online micro-clustering and offline integration. Subsequently, we develop the C-BOUND algorithm to extract the edges of the clustering results. In response to alarms associated with multiple operating conditions, segmentations are conducted to set alarm threshold groups and build a multi-condition alarm threshold model. Consequently, an adaptive alarm threshold setting method based on model matching is created. The effectiveness of the proposed method is demonstrated by experiments on a coal gasification chemical process. The proposed method provides a potential application for industrial processes with multiple operating conditions alarm managements.

Incipient fault detection for geological drilling processes using multivariate generalized Gaussian distributions and Kullback–Leibler divergence

Early detection of downhole faults in geological drilling processes can effectively reduce downtime and prevent catastrophic drilling accidents. Considering that the changes of drilling signals in early faults are difficult to observe while the data distribution may have significant deviations, a systematic incipient fault detection method is proposed for drilling processes based on the Multivariate Generalized Gaussian Distributions (MGGDs) and Kullback-Leibler Divergence (KLD). The contributions are twofold: (1) A new dissimilarity index is proposed for incipient fault detection by estimating the KLD between the MGGD of the historical data under the normal condition and that of the real-time data; (2) an adaptive alarm limit design approach is proposed for updating the alarm limits that adapt to the increasing of the drilling depth. Industrial case studies from a real drilling project are presented to demonstrate the effectiveness and practicability of the proposed method.

Data Analytics Method For Detecting Extinction Precursors To Lean Blowout In Spray Flames

ABSTRACT Aircraft engines must always maintain a margin between the operating equivalence ratio and the lean blowout boundary because flame-out presents a significant risk to the safety of the aircraft. It is believed that flames undergo a series of extinction/re-ignition phenomena before blowout. Previous attempts to characterize these phenomena have not been universally accepted. The approach presented here is from data analytics and consists of three parts: data curation, fault detection, and an adaptive alarm reliability assessment. The data curation filters the nonstationary behavior from photomultiplier tube signals recorded from a combustor test-rig, thereby reducing the number of false alarms. The filtered data is used to develop a fault detection algorithm that detects changes in the statistical properties of the signal. This results in alarms that serve as precursors of impending blowout. By leveraging information from previous blowout occurrences and the currently observed signal, the reliability of these alarms is updated in an adaptive manner. Through this methodology, combustion system operators are provided a means for assessing the proximity of blowout in a probabilistic manner.

Abnormality Detection for Drilling Processes Based on Jensen–Shannon Divergence and Adaptive Alarm Limits

Accurately and promptly detecting downhole abnormalities is of great importance to ensure safe and efficient operation of geological drilling systems. In view of the lack of abnormal data and the presence of multiple operating states, this article proposes a new abnormality detection method for geological drilling processes based on the Jensen-Shannon divergence and adaptive alarm limits. The major contributions are twofold: 1) a novel framework is proposed to detect downhole abnormalities by identifying normal operating conditions first and then establishing the normal operating zones; 2) an adaptive alarm limit design method is proposed to determine the normal operating zones by modeling the relation between the distribution feature and the variational trend feature based on the boundary points. The effectiveness and practicability of the proposed method are demonstrated by an industrial case study. The results demonstrate that the proposed method has superior performance to other methods in downhole abnormality detection.

Analysis and Examination of Heart distress from ECG signal using Artificial Intelligence.

Health data analysis is usually based on a comparison of derived health measures to predefined thresholds. Symptoms can be observed if a value is above or below a threshold. Early detection of signs of heart failure allows the prediction of strokes of heart failure and can therefore prevent these. So identifying “accurate” criteria is the most important task. The accuracy of an experiment depends strongly on the accuracy of the criteria used. Congestive heart failure (CHF) occurs when the heart can not pump sufficient blood for a stable physiological condition. CHF usually occurs when the coronary artery blockage causes the heart tissue to become acidic. The data used to analyze data such as Linear Regression, Missing Enrollment Data, Search Signal, Clinical Data Protection Programs, and Early Adaptive Alarm. The proposed system involves models including server and data warehouse processing, pre-processing, extraction classification characterization in this paper. Classification of heart defects and prediction of heart failure by using applied classifier for hybridization, guideline for treating patients as a gym, level of stress management. In this article, the program tracks the heart disease patients, predicts atrial fibrillation and ventricular fibrillation, and alerts patients when the critical condition occurs.

Validation of an Adaptive Alarm Interface Design for Digital Control Panels

ABSTRACTThis study proposes an adaptive alarm interface, which is supposed to have the good features of both alarm–tile interfaces that are typical in nuclear power plants (NPPs), and alarm–bar interfaces that use changing bars to indicate the current condition of an NPP and its abnormalities. Depending on the situation, the alarm management system will decide whether the alarm–tile or the alarm–bar interface should be presented to operators. To validate the usability of the adaptive alarm interface, it was compared against both the alarm–tile and alarm–bar interfaces through laboratory experiments with 45 participants. The results demonstrated that the adaptive alarm interface not only allowed the understanding of parameter trending, which was a distinguishing feature of the alarm–bar interface, but also improved the alarm detection, which was one advantage of the alarm–tile interface. However, the situation awareness level of the adaptive alarm interface was insufficient compared to the alarm–tile and bar interfaces possibly due to distraction from interface swapping.

A Vibrotactile Alarm System for Pleasant Awakening.

There has been a vast development of personal informatics devices combining sleep monitoring with alarm systems, in order to find an optimal time to awaken a sleeping person in a pleasant way. Most of these systems implement auditory feedback, which is not always pleasant and may disturb other sleepers. We present an adaptive alarm system that detects sleeping cycles and triggers alarm signal during shallow sleep, to minimize sleep inertia. Since tactile sensation is associated with positive valence, vibrotactile stimulation is investigated as a silent alarm to enhance pleasant awakening. Three modulation techniques to render the tactile stimuli for pleasant awakening are considered, namely simultaneous, continuous, and successive stimulation. Two experimental studied are conducted. Experiment 1 studied exogenous attention towards tactile stimulation in a multimodal scenario (involving visual and haptic interactions) with fully awake individuals. Results from the attention task and the subjective valence rating suggest that the vibrotactile stimulation should be based on the continuous modulation, since this not only is very perceivable but also associated with positive attention. Experiment 2 evaluated the user experience with tactile stimulation patterns during sleep. Results confirmed the findings of experiment 1. Continuous modulation was rated highest for pleasant yet arousing sleep-awake transition.

A Fiber-Gating Intelligent Adaptive Alarm Algorithm Based on Wavelet Analysis

For Fiber-Grating Perimeter-Intrusion alarm system, a Fiber-Grating intelligent adaptive alarm algorithm based on wavelet analysis and difference algorithm was proposed. The algorithm precedes the real-time waveform by wavelets decomposition, and acquired the assertion values of each subband by difference calculation. Through the method of comparing the assertion value of some subband, intelligent adaptive alarm system was realized. The experiments indicate that the proposed algorithm can give an alarm for illegal intrusion with good performance and false alarm below 1.5%, and it provides a promising application prospect for intelligent adaptive alarm.

Model-Based Detection of Hydrogen Leaks in a Fuel Cell Stack

Hydrogen leaks are potentially dangerous faults in fuel cell systems that are fed with hydrogen-rich gas mixtures. This brief presents an approach to hydrogen leak detection and, thus, complements direct detection using hydrogen sensors. It relies on simple mass balance equations of an anode filling volume after taking into account the natural leak of the stack. A hydrogen mass flow, anode pressure, and relative humidity sensor are employed. Hydrogen leak detection without the use of relative humidity sensors is considered by employing adaptive alarm thresholds to eliminate false alarms. The validity of the method is also discussed in terms of common hydrogen supply system configurations. The detection method is validated using a 1.25-kW polymer electrolyte membrane fuel cell stack in a laboratory facility where leaks could be introduced in a controlled manner.

Adapting Alarm Threshold to Driver's Brake Timing: Is it More Effective and/or Acceptable than Stopping Distance Algorithm?

In order to develop a useful forward collision warning system, it is important to determine the alarm timing appropriately. This study examined whether or not adapting alarm timing to driver's brake timing is more effective and/or acceptable by drivers than conventional Stopping Distance Algorithm (SDA). The results suggest that the proposed method can contribute to prevent rear-end collisions, and is as acceptable as SDA. Also, the driver adaptive alarm system may not suffer from driver's over-reliance.

3116 ドライバの運転操作にもとづく追突警報タイミングとドライバ反応(非拘束モニタリングと追突防止,OS4 ヒューマンインターフェース)

We investigated a driver adaptive alarm which is triggered based on individual driving styles. Two kinds of alarm timing were considered in this research: (1) An alarm is given at the timing of ordinary accelerator releasing, (2) An alarm was given at the timing of ordinary brake implementing. We compared the two alarm timings and found that both alarm timings were acceptable from the view point of trust in warning systems. However, for drivers who have particular characteristics of driving style, e.g., drivers who tend to implement the brakes very early or very late adapting the alarm timing simply to the brake implementation timing may impair the alarm effectiveness.

Adaptive alarm system

This invention provides an alarm system and method for adjusting the wake-up signals. The system includes a means for tracking the behavior of a person in a predetermined area under surveillance after the activation of an alarm clock, and a means for determining whether the person is motionless for a predetermined time period. Upon recognition that the observed behavior indicates the person is still sleeping, the wake-up signals are gradually increased. At the same time, the electrical power supplied to a plurality of electronic devices may be increased to assist the person to wake up.

Historical aspects of anxiety

"Anxiety" is a key term for behavioral, psychoanalytic, neuroendocrine, and psychopharmacological observations and theories. Commenting on its historical aspects is difficult, since history is properly a study of primary data. Unfortunately, much clinical anecdote does not correspond to factual records of a long time ago. Even reports of objective studies may suffer from allegiance effects. This essay therefore primarily reflects the personal impact of others' work against the background of my experiences, clinical and scientific. These lead me to question the assumption that "anxiety", as it exists in syndromal disturbances, is simply the quantitative extreme of the normal "anxiety" that occurs during the anticipation of danger. An alternative view that emphasizes dysfunctions of distinct evolved adaptive alarm systems is presented.

Enhancing Operator Control by Adaptive Alarm Presentation

The use of alarms in process control is a complex affair, because alarms can have many different functions over and above that of alerting the operator to a new event. Alarms are potentially a rich source of information that can be used in all phases of responding to a disturbance and should therefore be considered as an aspect of the general process information. From the point of view of cognitive systems engineering, alarms provide information that enables the operators to maintain control of the process and to be proactive as well as reactive. An important issue is the time and effort needed to evaluate events and new information (time to evaluate: TE) and the time and effort needed to select an appropriate response or line of action (time to select: TS). The former (TE) can be supported by more effective presentation of information, specifically a more comprehensive presentation of alarms. The latter (TS) can be supported by various kinds of computerized support, ranging from planning systems to adaptive and computerized procedures.

Adaptive alarm processor for fault diagnosis on power transmission networks

In this paper we describe a learning classifier system (LCS) which employs genetic algorithms (GA) for adaptive on-line diagnosis of power transmission network faults. The system monitors switchgear indications produced by a transmission network, reporting fault diagnoses on any patterns indicative of faulted components. The system evaluates the accuracy of diagnoses via a fault simulator developed by National Grid Co. and adapts to reflect the current network topology by use of genetic algorithms.