Using Data Science For Reliable Lift Operations in Singapore
By: Surbana Jurong Data Science Team
Tags: Data Science, Elevator And Escalator, Machine Learning, IoT

Having reliable and highly available lifts in Singapore is one of the key aspects which the Building and Construction Authority (BCA)¹ is encouraging lift owners to aim for. There has been great emphasis on modernizing existing lifts to further improve their operational standard over the last decade. With more high-rise buildings being constructed in Singapore, the need for highly reliable lift operation is something both lift manufacturers, and the government are keen to work on. Surbana Jurong (SJ) has been instrumental in achieving the same over the last few years with our Lift Monitoring System (LMS) solution installed at over 26,000 HDB lifts in Singapore. The solution leverages on lift operation data gathered from various IoT sensors. The goal of SJ’s Smart City LMS solution is to analyse lift performance by combining data-driven approach with planned maintenance activity to reduce unplanned downtime and improve safety and reliability of lifts in Singapore.

Data-Driven Architecture For Preventing Lift Faults

With the use of modern infrastructure in lifts, many lift manufacturers have incorporated non-intrusive IoT based sensors and edge devices.² Using real-time data from each lift, SJ’s LMS solution monitors and tracks performance of the assets for fault prevention. LMS solution is hence a combination of Lift Monitoring Device (LMD, used for status-based analysis) and Lift Monitoring Performance Device (LMPD, for time series data from raw signal). The data is stored in cloud and functions with Microsoft Azure suite of services for secure storage and faster analysis. Unsupervised machine learning models are then used to detect anomalies in the LMS back end. If our machine learning models detects fault signatures, LMS triggers SJ’s Smart Incident Management System (SIMS) to activate lift contractors for maintenance. Hence, this is comprehensive end-to-end solution for optimizing maintenance activity and reducing downtime.

Figure 1. End-to-End solution of SJ Lift Monitoring System

Finding Lift Fault Patterns With SJ’s Proprietary Lift Sensors Feature Store

Large volume of data gets generated with every trip operation across the lifts. For example, more than 2.5 million trips were recorded in 2020 from 53 lifts at an academic institution in Singapore. Raw signals are generated from accelerometer and door current IoT sensors. Feature engineering is done on raw signals from accelerometer and door current IoT sensors. Meaningful signal features like maximum acceleration, deceleration, jerk and their frequency domain components are extracted from accelerometer data. These are combined with statistical features like mean, variance and skewness to get an exhaustive pool of features from raw signal. Similar feature extraction is done for door current signal as well.

Feature engineering is a complicated yet critical component for any machine learning process. Our team developed a ‘Lift fault feature store’ using these numerous features. Feature store enables consistent feature set for training ML models. Our data scientists can reuse features as and when changes are made to the models or when new models are used for deployment. This saves valuable time and effort in the MLOps pipeline and encourages quick experimentation.

Figure 2. Segmentation of Door Open and Close regions and feature extraction from raw current data

Figure 2 shows door current feature engineering algorithm extracting important features by segmenting current signal into door open and close regions. After feature engineering, anomaly detection module deployed at edge devices use machine learning methodologies like unsupervised machine learning to identify fault indicators for each trip. Unsupervised machine learning models like unsupervised KNN and cluster-based local outlier factor (CBLOF) are trained and tested on the feature store consisting of few months of historical data to classify each trip into two categories — abnormal and normal trips. The trained model is then deployed to detect abnormal features and classify each trip accordingly. Historical trends for the trips classified as abnormal are then analysed to check if signal features are deteriorating over time. Performance of each lift is also compared with lifts of same brand (ex: Hitachi, Sigma) and any major deviations are notified to the lift vendor for early maintenance.

Actionable Operational Insights For Customers And Lift Manufacturers

Using data from IoT sensors and analyzing it over time, multiple insights could be shared with our clients. One such instance is seen for our academic institution customer where SJ’s LMS solution is deployed.

Figure 3. Increase in number of values of max deceleration above 95th percentile

The lift installed at one of the academic blocks had seen deterioration in maximum deceleration. Figure 3 shows gradual increase in percentage number of trips above 95th percentile over the last few months. The issue was shared and discussed with the customer and lift manufacturer. Checks were done on the lift motor to investigate early signs of failure.

Insights From Long-Term Trends To Drive Better Lift Designs

Apart from fault detection mechanism, our team also analyses historical faults over the last few years for customers to get some insights on factors which are linked to various fault categories. Useful suggestions could be made to lift companies and major fault categories could be investigated and rectified during planned maintenance.

Figure 4. Distribution of number of faults by Fault types

As seen from Figure 4, majority of lift faults are related to mechanical failures. Hence, rectifying these issues take most of the planned and unplanned maintenance time. Lift manufacturers could focus on maintaining the mechanical integrity of lifts to reduce unplanned downtime.

Also, merging the data of lift operating information and faults over the last few years, some more interesting insights were found.

Figure 5. Variation of Average Fault for Low, Mid, High rise buildings by Drive Type

Figure 5 shows the variation of average fault per lift for buildings categorized by storeys and drive type. As Singapore has a lot of high-rise buildings, we look specifically into variation of average fault per lift for high rises. Our analysis on the last 3 years of data show that Drive Type 1 has reduction in average fault per lift by 34% for high rises and is more reliable as compared to Drive Type 2. So, this drive type could be considered by lift companies to reduce lift faults for high rises.

Lift analytics project is an ongoing effort by SJ as it is committed to have more safer and reliable lift operations across Singapore. As LMS solution is being widely deployed across various sectors and countries, it is much more important to have a data driven approach for cutting down unplanned maintenance activity. We are excited to take up further challenges and expand our work with other customers in Singapore and other parts of the world.

About the writer

Nishant works as executive data scientist for smart city project in Surbana Technologies and has over 7 years of experience combined in Engineering & Analytics.

He graduated with a bachelor’s degree in Electrical & Electronics Engineering (Honours) from National Institute of Technology (India) and started his career in Engineering. As a Senior System Engineer at Siemens, he contributed to development of Control schemes for motor management devices used for Industrial Automation. He later pursued his Masters in Power Engineering from Nanyang Technological University, Singapore. He joined as a Researcher at Rolls Royce Corporate Lab after his masters and was involved in research of predictive algorithm using mathematical physics-based modelling of electrical motors. He further worked as a Reliability Engineer in GE Digital and helped customers save maintenance cost by analyzing faults before breakdown in industrial equipment across various industries like Power, Oil and Gas, Renewable, etc.

Later he joined Surbana Technologies as a Data Scientist to pursue his interests in data analysis and machine learning.