---
title: Connected elevator solution using Predix IoT Stack
type: article
date: 2018-12-01
source: website
original_url: "https://navveenbalani.dev/index.php/articles/connected-elevator-solution-using-predix-iot-stack/"
topics: ["iot"]
legacy_categories: ["iot"]
tags: ["iot-guide"]
summary: "This article is part of IoT Architecture Series -
In [previous article](/articles/building-application-with-ge-predix-iot-platform) we went through the Predix IoT Stack. In this article, we will go through implementing the [connected elevator solution](/articles/internet-of-things-application-of-iot-in-manufacturing) using Predix IoT Stack
#### Hardware and Connectivity
In this section, we would discuss the hardware and connectivity approach for the elevator use case. We will realize the elevator use case using Predix IoT stack through the following phases we described in [earlier article](/articles/internet-of-things-application-of-iot-in-manufacturing) - viz. monitoring (which includes asset management), condition maintenance and finally predictive maintenance.
We would not go into the details of each phase again but only talk about the implementation steps. The manufacturer as part of his connected design exercise adds various new sensors to the existing elevator control unit to realize the requirements as mentioned in earlier article. The elevator manufacturer as a first step leverages its existing investment and tries to keep the connectivity and communication protocol between elevator control unit and rest of components same and installs the Predix Machine on the elevator machine controller. The Predix Machine acts as a gateway providing local storage, analytics, and filtering of events and also provides secured communication with the Predix Cloud. The Predix Machine component wraps the filtered data into JSON data and sends it over MQTT protocol to Predix Cloud platform. Through the Predix EdgeManager Web interface, the device is remotely monitored and managed.
#### Software Implementation Overview
The following are the high-level steps that needs
to be performed to build the IoT application
1. Generate the Predix Machine OSGI container artifact containing
the required features using Predix
Machine SDK.
2. Register devices and get access
credentials through EdgeManager.
3. Provision devices (elevator
control device) through EdgeManager and install the Predix Machine OSGI container from Step 1 on the devices.
4. Create storage services (Create
the asset model and store the model and instances using Asset Data service and
create Time Series service for storing raw time series data from devices).
5. Configure Message Topics for
communication from devices to Cloud and various Cloud services.
6. Create Analytical Code.
7. Deploy Analytics code in
Analytics Catalog.
8. Create Analytics Orchestration
configuration for the analytical code and execute analytics orchestration using
Analytics runtime.
9. Create web dashboards and/or create Mobile applications using Mobile
SDK for remote monitoring and management.
*Note –
We choose to use an example of running elevator machinery 24\*7, instead of
picking up a complex industrial use case like oil machinery or water management
plant. The steps outlined would be pretty much the same as described above with
the addition of complexities around integration with existing industrial
devices using protocols like ModBus or setting up a secured compliant network
infrastructure from existing manufacturing plants to Predix Cloud.*
The above implementation steps are just one of the approaches for building end to end IoT application. The following shows the connected elevator solution using Predix cloud platform and services.
The RabbitMQ service in Predix Cloud receives
the data from the connected elevator (controller device) over MQTT protocol.
The controller device has Predix Machine software stack installed and uses the
MQTT River component to send data to configured topics in RabbitMQ. A custom
service is deployed which subscribes to
the configured topic and uses the Time Series service to store the time series
data. The time series data represents a
timestamp and value for each sensors (like temperature, pressure , load etc)
which are continuously streamed from the devices, which can be stored and
filtered by Predix Machine software component on the elevator control device to
do local analysis and/or send it directly to the Predix cloud for analysis.
This provides an option of doing simple
condition based monitoring and triggering alerts locally and also send only the
relevant information over to the cloud.
The real advantage is that the device can be
remotely monitored, and the software stack can be updated remotely to
add new customizations.
*Tip -
We could also have used WebSocket protocol instead of MQTT. For supporting
WebSocket protocol, we need to use WebSocket River component in Predix Machine
software stack to connect to the Predix
cloud. The WebSocket River component can also directly post the time series
data from devices/sensors to Time Series Gateway component in Predix Cloud.*
The Analytics orchestration component is
triggered based on message arrival on the specified topic, and the required analytic workflow (it can be a single
workflow containing your analytical code) is
executed by the analytical engine/runtime.
For the elevator use case, two analytical components are developed, one for
detecting anomaly and other for predictive
analytics. The analytics code uses machine learning models and is developed
using Python and scikit-learn library.
The machine learning model uses time series data, asset data, and historical records as inputs for analysis and predictions.
The analytics code is uploaded to Predix Analytics catalog using Predix Analytics User service. Any dependent libraries need to be
specified in a configuration file as per the analytics development
process (reference - )
Once the analytic code is tested, you promote it to production state,
where it is executed by the analytics runtime.
*Tip -
As mentioned earlier, the approach of building a machine learning model,
whether using Microsoft, Amazon or IBM
tools or open source options like Python scikit-learn
library remains the same. You build the model and train the model iteratively
using the raw data from the connected devices, asset metadata and historical
maintenance records. The real challenge in building the model is choosing the
right features, cleaning( or filtering)
and aggregating the data, applying algorithms, training the model and
continuously iterating it to reasonably predict
the outcome.*
Based on the response of analytics service
(if maintenance is predicted or
required), the maintenance workflow service is triggered. The status is
updated in the Postgre SQL database which keeps the track of maintenance
activity.
A web dashboard is developed which pulls the data from the data sources (time
series, asset data and Postgre SQL database) that shows real-time updates on
the entire process and provides monitoring of the assets. The same information is also made available
in mobile applications and can also trigger/notify users to take corrective
action. The mobile application is developed using Predix Mobile SDK framework, which enables quickly building
applications using standards like HTM5 and JavaScript.
This completes an end-to-end execution cycle using Predix cloud.
In the next article, [we will look at how to build IoT solutions using Open Source stack](/articles/building-application-with-open-source-iot-stack)