Plug-and-play fault monitoring systems can make for fast detection of lift faults.
by Isaac Skog
To adapt vertical-transportation systems to future demands, the lift industry has identified a need to move from today’s preventive and corrective maintenance strategies to predictive- maintenance strategies. Therefore, the latest generation of high-end lift systems are often connected to the cloud, where control-system data are used to gather information about the lift systems’ health and potential faults. However, many lifts today are not equipped with control systems that support remote monitoring, and it is expected to take decades before they are all upgraded with them. Moreover, even if they do have control systems that support remote monitoring, the cost and time of connecting the lifts to the cloud and then merging the data from lifts of different makes and types into a single monitoring system are often extremely high and lengthy.
There is a need for plug-and-play sensor nodes and cloud- based monitoring services to which existing lift systems, independent of make and type, can be upgraded and connected for condition and fault monitoring. To meet these needs, SafeLine of Sweden developed the “smart” Internet of Things (IoT) sensor node LYRA and the cloud service ORION. Using built-in accelerometer and magnetometer sensors, LYRA can extract information about the lift’s position, speed and acceleration; vibration levels (according to the ISO 18738 standard) and vibration spectrums; and door motions and openings.[2-4] However, as the LYRA node is self-contained and not connected to the lift’s control system, it cannot determine if the absence of trips during a time period is due to the lift system not functioning properly or just because no calls for the car have been placed. Therefore, a new machine-learning- based method to determine if the lift system is operational or dysfunctional, given that no travels have been observed within a time window, has been developed.
Detecting Unusually Long Stationary Periods
The problem of determining if a lift system is operational, given that no travels have been observed within a time window, can be divided into two parts. Part one is to define a hypothesis test that, given a model for how the traffic load varies with time, determines if the absence of travels during a time period is caused by the lift system being dysfunctional. The second part is to, from historical travel data, learn a model for how the traffic load on the lift system varies with time.
Isaac Skog is an assistant professor at Linköping University in Linköping, Sweden, where he conducts research on joint sensor fusion and machine learning. He is also founder of S3 Research, which conducts R&D in predictive maintenance and machine learning. He received his PhD in Signal Processing from the KTH Royal Institute of Technology in Stockholm, Sweden, in 2015.