An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability

The recurrent neural network and its variants have shown great success in processing sequences in recent years. However, this deep neural network has not aroused much attention in anomaly detection through predictively process monitoring. Furthermore, the traditional statistic models work on assumptions and hypothesis tests, while neural network (NN) models do not need that many assumptions. This flexibility enables NN models to work efficiently on data with time-varying variability, a common inherent aspect of data in practice. This paper explores the ability of the recurrent neural network structure to monitor processes and proposes a control chart based on long short-term memory (LSTM) prediction intervals for data with time-varying variability. The simulation studies provide empirical evidence that the proposed model outperforms other NN-based predictive monitoring methods for mean shift detection. The proposed method is also applied to time series sensor data, which confirms that the proposed method is an effective technique for detecting abnormalities.Comment: 19 pages, 9 figures, 6 table

Remaining Useful Life Modelling with an Escalator Health Condition Analytic System

The refurbishment of an escalator is usually linked with its design life as recommended by the manufacturer. However, the actual useful life of an escalator should be determined by its operating condition which is affected by the runtime, workload, maintenance quality, vibration, etc., rather than age only. The objective of this project is to develop a comprehensive health condition analytic system for escalators to support refurbishment decisions. The analytic system consists of four parts: 1) online data gathering and processing; 2) a dashboard for condition monitoring; 3) a health index model; and 4) remaining useful life prediction. The results can be used for a) predicting the remaining useful life of the escalators, in order to support asset replacement planning and b) monitoring the real-time condition of escalators; including alerts when vibration exceeds the threshold and signal diagnosis, giving an indication of possible root cause (components) of the alert signal.Comment: 14 pages, 12 figures, 7 table

An Algorithm for Modelling Escalator Fixed Loss Energy for PHM and sustainable energy usage

Prognostic Health Management (PHM) is designed to assess and monitor the health status of systems, anticipate the onset of potential failure, and prevent unplanned downtime. In recent decades, collecting massive amounts of real-time sensor data enabled condition monitoring (CM) and consequently, detection of abnormalities to support maintenance decision-making. Additionally, the utilization of PHM techniques can support energy sustainability efforts by optimizing energy usage and identifying opportunities for energy-saving measures. Escalators are efficient machines for transporting people and goods, and measuring energy consumption in time can facilitate PHM of escalators. Fixed loss energy, or no-load energy, of escalators denotes the energy consumption by an unloaded escalator. Fixed loss energy varies over time indicating varying operating conditions. In this paper, we propose to use escalators' fixed loss energy for PHM. We propose an approach to compute daily fixed loss energy based on energy consumption sensor data. The proposed approach is validated using a set of experimental data. The advantages and disadvantages of each approach are also presented, and recommendations are given. Finally, to illustrate PHM, we set up an EWMA chart for monitoring the fixed loss over time and demonstrate the potential in reducing energy costs associated with escalator operation

Remembering George E.P. Box with: Inez M. Zwetsloot (University of Amsterdam) Curs Box 2023-2024

Remembering George E.P. Box with: Inez M. Zwetsloot (University of Amsterdam) Curs Box 2023-202

Decreasing the dispatch time of medical reports sent from hospital to primary care with Lean Six Sigma

Timely communication is important to ensure high-quality health care. To facilitate this, the Gastro Intestinal Oncology Center Amsterdam (GIOCA) stipulated to dispatch medical reports on the day of the patient's visit. However, with the increasing number of patients, administrative processes at GIOCA were under pressure, and this standard was not met for the majority of patients. The aim and objective of this study was to dispatch 90% of medical reports on the day of the patient's visit by improving the logistic process. To assess the main causes for a prolonged dispatch time and to design improvements actions, the roadmap offered by Lean Six Sigma (LSS) was used, consisting of five phases: Define, Measure, Analyze, Improve and Control (DMAIC roadmap). Initially, 12.3% of the reports were dispatched on the day of the patient's visit. Three causes for a prolonged dispatch time were identified: (1) determining which doctors involved with treatment would compose the report; (2) the reports composed by a senior resident had to be reviewed by a medical specialist; and (3) a medical specialist had to authorize the administration to dispatch the reports. To circumvent these causes, a digital form was implemented in the electronic medical record that could be completed during the multidisciplinary team meeting. After implementation, 90.6% of the reports were dispatched on the day of the visit. The dispatch time of reports sent from hospital to primary care can be significantly reduced using Lean Six Sigma, improving the communication between hospital and primary car