No abstract

In ternet: Seppo.Rnn t ala@kau. v t t . fi, Risto.Suoran ta@kau. vt t .fi ABSTRACTIn this work, we have applied a parametric modelling technique to the predictive maintenance of rotating machinery. The case, which we will explain in details, was taken from an experiment, where an one-step gearbox was run at about 150% of nominal load until failure occurred. During the experiment vibration signals from the gearbox were measured.The novelty of this work is to analyze the residual signal obtained by computing the difference between the predicted and measured signal. Parametric modelling technique called autoregressive model is utilized in prediction procedure. The analysis of the residual signal prove to be successful; the failure could be predicted easier and earlier than using traditional FFT-based nietliods.

The city of Tampere in Finland aims to be carbon-neutral in 2030 and wanted to find out how the electrification of public transport would help achieve the climate goal. Research has covered topics related to electric buses, ranging from battery technologies to lifecycle assessment and cost analysis. However, less is known about electric city buses’ performance in cold climatic zones. This study collected and analysed weather and electric city bus data to understand the effects of temperature and weather conditions on the electric buses’ efficiency. Data were collected from four battery-electric buses and one hybrid bus as a reference. The buses were fast-charged at the market and slow-charged at the depot. The test route ran downtown. The study finds that the average energy consumption of the buses during winter was 40–45% higher than in summer (kWh/km). The effect of cabin cooling is minor compared to the cabin heating energy needs. The study also finds that infrastructure needs to have enough safety margins in case of faults and additional energy consumption in harsh weather conditions. In addition, appropriate training for operators, maintenance and other personnel is needed to avoid disturbances caused by charging and excessive energy consumption by driving style.

In the rail traffic industry, the utilisation of inexpensive real-time sensors and the industrial internet of things for proactive asset management is a relatively new concept with great potential. As railways are one of the longest-lasting infrastructure assets, even marginal efficiency and cost gains have a significant impact on the life-cycle cost. This paper shows how wireless three-dimensional acceleration sensor technology can be applied to monitor track condition. The data collection was carried out in October 2016 on a railway line operated by Finnish Railways. In the test, a sensor was attached to a train unit and the acceleration of the train on a track segment was repeatedly measured at variable speeds. The collected data set was enhanced using map-matching and Bayesian filtering in order to improve the Global Positioning System location accuracy of the data. The filtered acceleration signals were analysed, and detected anomalies were compared against known parameters such as bridges and switches. The results of the testing support the feasibility of the concept. Finally, the implications of the concept regarding proactive asset management of track networks and statistical process control-based monitoring of tracks’ condition are discussed.

No abstract