Category: Case Studies

Categories
Case Studies

Diagnostics based Asset Predictive Maintenance Prelude

Diagnostics based Asset Predictive Maintenance

AI & MACHINE LEARNING CASE STUDY – INSIGHTFUL IIOT

Predictive Analytics Solutions

Prelude: In the wake of sensor enabled devices, pervasive digitization, big data platforms and large viable analytics combined with artificial intelligence acceptance took automation diaspora into a different level. Though there are several tools catering to different markets, sizes and verticals, a simple, scalable, actionable and affordable solutions are yet to mature in the present market.

Introduction to Our Solution: Our in-house solution caters to complete data engineering life cycle that begins with cumulating sensor data into data lakes, pre-processing and data preparation, apply ML and AI algorithm wrappers catering to several business cases and showcase the results in interactive dashboards

Solution Highlights

Analyze and diagnose the historical equipment data that has logged several component errors such as overheat, hoist, and break faults. Accordingly, predict and raise the relevant flags and alerts intimating the stakeholders for quick and timely actions

Ingest Data

Cranes operational data gathered from various sensors across geo locations was ingested into AWS storage

Build the Model

Custom ARIMA models Built toforecast diverse faults across several bins

Generate Insights

Predict the imminent faults of different components

The solution used ARIMA algorithm to predict the overall fault volumes as well as location and machinery specific faults

Generated proactive alerts for each machinery highlighting the possible event such as hoist or brake failure

Effective planning of maintenance schedules, shipment of spare parts, and service engineer visits

Reduce maintenance/downtime delays and thus improve overall utilization by 7%

Proactive alerts based on sensitivity and severity (RAG) status

Solution Components & Benefits

CORE TECHNICAL COMPONENTS USED:
  • Implementation of supervised and unsupervised clustering models
  • Successful implementation of time series models based on ARIMA over Holt Winters for ‘Accurate Forecast’
  • Implementation of ‘Survival Models’ based on Cox PH Algorithms
  • Overall Simulation was compiled on R and run on AWS Highly Available Cloud Infrastructure

“Solution was developed across 10000+ machinery assets and the predictive models were able to generate accuracies upto 85% in fault prediction diagnostics”

Categories
Case Studies

ICCC- Water Management System (Smart City)

ICCC- Water Management System (Smart City)

Industrial Solution Deep Dive

As a part of this section, we will be looking at monitoring Pump house and its efficiency, prediction of faults (pre alarm conditions), KPI (Power Vs Water distribution) and demand analysis, leveraging AI and IoT giving both Industrial Solution Deep Dive as well as detailing about the Application stack leverages. The section would include the following:

Industrial Solution Deep Dive

  • Current Challenges
  • Solution & Tech Components
  • Lean Architecture & features
Current Challenges – Overview
  • Water distribution pump houses are equipped with Pump, motor, starter panels, valves etc. basic infrastructure.
  • Electro mechanical parameters plays important role for citizen water distribution system. i.e. Water Flow, pressure, Energy Measurement, Level etc,
  • Municipal officials are concerned about KPI (Key Performance Indicator) of Total Flow during the day (MLD) Vs Energy Use to distribute the water. (KWH / KL). The above KPI will decide overall efficiency of the system.
  • Less the Power greater the efficiency.
  • Minimum Water Supply is also a prime responsibility and requires all infrastructure to work without fail.
Solutions and Tech Components

Use of technology in the infrastructure

  • To monitor the pre-alarm condition of Motor failure AI based programming with smart sensor technology will help.
  • Any Power supply related issues to be monitored
  • Various factors affects the motor life and leads to failure.
  • Vibration of motor
  • Winding/body temperature of motor
  • All Power parameters i.e. Voltage, current, PF etc.
  • Pump efficiency measurement with Temperature, pressure, Head, Flow, etc.
Pump Efficiency Monitoring by using

Pump Energy Reporter Module

  • Base Line Calculator
  • Energy report
  • Pumping cost this hour, last hour, today, yesterday, last week, last month (KWh/m3, Cost per m3)
  • GHG Emission Report (GHG Gas emission today,Yesterday, last week, last month )

CASE STUDY:

This affect was seen system wide when implemented for a customer…

Lean Architecture and Features:

Intelligent Remote Terminal Unit is connected with various field sensor wired or wirelessly Field sensors connected with iRTU for different parameters monitoring are e.g

  • Vibration and temperature sensor—For Motor failure prediction
  • Camera or AI based sensor-Leakage inside Pump house
  • AI based Edge solutions – Panel monitoring via. Actual status of lamps and panel conditions
  • Pressure, Flow and temperature parameters for Pump efficiency monitoring

iRTU will connect via. GPRS (or various wireless communication technology NB-IOT, LoRa Wan, etc.) with Master Control station SCADA software (WebSCANET) and WebSCANET will send data through API or OPC-UA to ICCC software.

ICCC software can have below integration:

  • Water Management software
  • Water Quality data (residual Chlorine, pH, Turbidity parameters)
  • GIS survey mapping on google map interface.
  • Hydraulic data

ICCC DASHBOARD

Key Performance Indicators (KPI)
  • Plot Dial Graph for Required daily Flow dial vs Actual Flow on Present day
  • Bar Graph: Last Seven days Total Volume distributed
  • Bar Graph: Last seven days Energy consumption data
  • Bar Graphs : KWH/KL for 7 days Bar graph: % Efficiency of Pump per day (Average) or Pump wise
  • Bar Graph: Total Energy Saving Per day (Base Line consumption – Actual Consumption)/ Base Line consumption
Performance Trends:

Values in Tabular form and Online trends of following parameters:

  • Motor and Pump Sr. No.
  • Efficiency
  • Flow rate
  • Total Head
  • Total Power consumption
  • Discharge pressure
Group Performance

Values in Tabular form and Online trends of following parameters:

  • Total Efficiency
  • Total Head
  • Total Power consumption
  • Total Volume
Manufacturer Curves
  • Manufacturer Pump and Motor data sheet and curves with asset details
  • Attributes if all mapped elements like Type/Model/ Mfr/ Vintage/ Ratings of Pumps, starters etc. Size/ Type/ MOC/ Vintage of pipes, staging/capacity of GLSRs/
  • ELSRs etc. shall form the exhaustive database of the Asset Management software.
  • Provision for dynamic addition of new assets and deletion of discontinued assets.
  • Provision of update on existing assets (like increase in capacity, pipe diameter changes etc.
  • Provision of automation entries Valves, Pressure, Flow, Level instrument details.
  • Provision of upload of documents like manuals, drawings, excel sheet etc.
Alarms
  • All alarm conditions related with motor trip/fail conditions
  • Alarms related with Pump efficiency below set points
  • Alarms related with Total KWH increases or volume increases
  • Pre-alarm conditions and Power parameters alerts
Categories
Case Studies

Oil and Gas Pipe Line Monitoring System

Oil and Gas Pipe Line Monitoring System

Industrial Solution Deep Dive:

As a part of this section, we will be looking at tracking Oil and Gas pipe line monitoring system leveraging AI and IoT giving both Industrial Solution Deep Dive as well as detailing about the Application stack leverages. The section would include the following:

Industrial Solution Deep Dive

  • Current Challenges
  • Solution & Tech Components
  • Lean Architecture & features

Current Challenges – Overview

O&G Pipe lines are laid underground or above ground in kms of length. The pipe line carries costly and hazardous fuels with high pressure. Small cracks, corrosion causes leakage in the pipe line which may incur loss to the Company and at the same time may have huge loss on environment / human hazards.

As a part of Integrity management of these pipelines, the foot patrolling of ROU (Right of use) of the pipeline needs to be monitored to avoid any Third party activity resulting into leak or fire.

The ROU monitoring activity is set on the basis of class location(population zones) through which pipe line is passing.

Based on the current scenarios, O&G companies are making physical survey of the system at the Test Lead Points for pipeline corrosion monitoring installed @ 1-1.5 km distance near Pipe line path and Right of Use (ROU) for physical condition monitoring of pipeline.

Manual survey are being carried out at different time interval monthly or bi-monthly on 200 to 400 km of distance and report are to be fed into the existing software by surveyor.

The foot patrolling will be on Daily & Monthly basis as required.

Typical areas where the monitoring has been done manually and cause the chances of errors:

  • Line Patrol Man (LPM) should observe soil/ground surface condition on and adjacent to Pipeline /ROU (right of Use) area, indication of leak/construction activity other than company, encroachment and any other factors/activities affecting the safety and security of pipeline like Digging/Drilling/Boring/HDD etc. Patrolman will have to pay special attention to all Railway / Bridge / Road / River / Canal/Nala crossings.
  • If any serious observation / abnormality is found which may be required to be reported immediately, along with photograph & location details.
  • Any de-coloration of vegetation on the ROU is to be reported to investigate possible leak Each LPM shall keep their GPS tracking device always in charged and ON condition to monitor via GPS tracking portal.
  • Deep rooted trees which are considered threat to the pipeline shall be reported to Local Control Room along with detailed information including photograph
  • All vulnerable locations viz. river crossings, all cased Road / Railway / canal crossings, Encroachments, Excavation, Exposure, washout shall be checked with special care and to be reported with photograph
  • Reporting on all activities being carried out on the ROU like, excavation, digging, construction, encroachment, exposure, wash out, plantation, leak, soil erosion etc.
Current Challenges – #1

Lack of Technology Awareness & Adoption

  • Lack of Technology Awareness related to Artificial Intelligence & Related Technologies
  • Lack of success stories in the adoption rate and churn curve
  • Accuracy in Prediction Models for specific Tasks
  • Joint Development considering customer pain point and need
Solutions and Tech Components

Use of technology in the infrastructure

  • GPS co-ordinates at TLP locations and critical areas
  • Web based software Plotting all GPS co-ordinates
  • Artificial Intelligence based Virtual Drone image analytics
  • AI help to identify old photos and compare new Google images for Encroachment, Tree and vegetation near ROU
  • TLP unit to monitor Reference cell potential to identify pipes are protected from corrosion.
  • TLP can monitor any Tilt (damage) to installed Unit
Lean Architecture and Features: TLP Monitoring System
  • GSM based remote monitoring system
  • Consists of a high accuracy, compact, Battery Powered Unit with built‐in GSM Modem
  • Interface for continuous remote monitoring of Reference potentials, AC interference voltage and pipe Current at CP test stations
  • High Input impedance channel for Ref. cell monitoring
  • Device tampering Alert message to user
  • Sleep-mode for long battery life
  • Data logging via. Local interface i.e. RS-485/USB
  • Remote communication to Web server
GSM Based Cathodic Protection Monitoring:

Get your TR Status, Mode of Operation, Real Time Voltage, Current and PSP on your mobile Handset.

Categories
Case Studies

Pump House Monitoring with ‘Edge Computing’ Platform

Pump House Monitoring with ‘Edge Computing’ Platform

Overview

This use case describes how ‘Edge Computing’ can apply not just to cater very high end, large infrastructure and complex processing applications but also very useful for small to medium scale applications as widely in demand by today’s fast growing industry world. With ‘Pump House Monitoring’ application example, it demonstrates various advantages the water utility sector can gain over conventional solutions.

Basic Application

Water Pump House / Pumping Stations are mechanism that can be used to transport water from one site to another usually from its source to consumer e.g. supply water to canals, circulate water in treatment systems, supply water to residential areas etc. Few important parameters like pump motor input power supply, its power consumption, water flow, water pressure, time scheduled or demand based pump on & off control etc. are to be taken care for its smooth functioning so that to meet demand-supply in efficient manner. Again, this can be remote site set-up so all such monitoring & control need to be done remotely from utility’s back office (control station) over wireless / wired network connectivity.

Conventional Solution

Conventional solution utilizes below components -RTU or PLC

  • Cellular Modem or Ethernet for remote connectivity.
  • RS 232/RS 485 serial ports, analog inputs and outputs for energy meters, flow meters, pressure sensors etc.
  • Digital inputs and outputs for pump on-off control and basic security e.g. panel door open/close, power fail or any overload trip indication etc.
  • Limited memory for various event log/history data storage.

This is how Conventional Solution manages basic application. All required sensors / peripheral devices are wired to RTU / PLC, data logged into local memory, data processing, data sent to utility control station over cellular or Ethernet connectivity. It can log and generate power fail or any pre-set threshold limit alert messages as well for supply voltage fluctuation, overload trip etc. Store history data during connectivity loss. For any maintenance activity or fault recovery, operator needs to visit site and take corrective actions.

Challenges/Limitations with Conventional Solution

Though it can manage basic application in simple manner, it has few limitations as well.

High Network dependency to get real time alerts. This can be critical if user needs to take immediate corrective action in response and hence may increase system downtime.

Operator visit becomes must for any system level diagnosis & maintenance activity in case efficiency loss reported. Such is not always favorable for the far remote sites or if located at high altitude, hilly area etc.

Wired connectivity with limited interface options for all peripherals installed inside or outside pump house premise that can lead to reliability issues.

No premise security, so theft threat. If needs to build this feature, requires additional system solution with IP Cameras & dedicated server including integration efforts to user control room in real-time manner.

Lack of preventive maintenance mechanism leads to high maintenance cost/failure recovery. To have this feature added in conventional system, need to add specific sensors along with pump, have added interface channels at RTU/PLC end, data capturing, logging and transporting mechanism which again demands for high end RTU/PLC solution, reliable connectivity for large data transport, software solutions/cloud components with data analytics for preventive maintenance. All these means more dependencies and cost addition.

Edge Computing Solution and it’s Components

Edge Computing is computing that’s done at or near the source of data, instead of relying on the cloud/server to do all complex analytics work. With this it is more efficient to cater data latency, bandwidth and security related issues hence attractive feature to have for any real time monitoring & control application solution. To have this feature, it demands for certain capabilities in control system solution depending on complexity of tasks involved with targeted application.

Leveraging this for pump house monitoring application, here we list out some of key features/components example to integrate with device solution or say build ‘Edge Computing Platform’

Required Components –

  • High speed (800 MHz~1 GHz) Processor
  • Linux Operating System -2GB RAM, 4GB Flash
  • Video Processing Unit (VPU), Image Processing Unit (IPU), 2D/3D Graphics Processing Unit (GPU), Asynchronous Sample Rate Converter (ASRC) etc. hardware accelerators
  • Pre-trained data analytics models for Motion Detection, Human Detection and Pump Health status monitoring etc. The models will be trained with ML frameworks, i.g. Tensorflow, Keras, Pytorch or Mxnet on a powerful system such as a Deep Learning server. Then the model that is trained will be exported to the edge device.
  • Open-CV or Pillow library for IP Camera initialization and Image/Video Pre-Processing
  • Cellular Modem, Ethernet Port, POE+ Port, RS 232/RS 485 Port
  • Analog and Digital inputs & outputs
  • Zigbee / Bluetooth wireless interface options
  • HART Interface option
Work Flow & Advantages with Edge Computing

Diagram here shows how edge computing platform can replace any conventional system solution.

  • It manages to read all those same parameters from various field sensors but with added option of wired and wireless connected sensors, e.g. Pump
  • Vibration Sensor, Water Leak Detector, IP Camera, HART Sensors etc.
  • After various data acquisition, platform processes various input parameters and pass required data to respective AI (Data Analytics) tool running there on Edge Platform.
  • With conclusive data output from all required data analytics, it controls back the application process as and when needed in real time
  • Generates if any alert on potential pump failure, water leakage, security concern etc. observed & report to control station. Also takes corrective action as Pre-programmed.
  • Log all events and report to control station as well as maintain history data for defined time duration.
Advantages
  • No critical dependency on network availability/control station connectivity as Edge Platform itself log, process, identify & take responsive actions in real time.
  • With powerful core inside, user can fully access/control the process remotely for any configuration updates or remote debugging.
  • Powerful core offers added wireless connectivity options for peripheral field sensors like Zigbee/Bluetooth which can solve any reliability issues in long wiring outside pump house premise.
  • Built in feature like HART not just monitors but also controls the configuration of HART based smart field sensors. Its purpose it to avoid human dependency incase any configuration change/calibration etc. required of filed sensor during its filed operation.
  • POE+ Port offers Power + Interface to IP Cameras. With image/video data analytics tools at Edge Platform, this can monitor premise security for any theft threat or un-authorized access.
  • With various pump operation parameters Data analytics at edge, it can predict pump health status. This saves lot of failure recovery cost by alerting maintenance team on potential pump failure situation in advance.
Solution Benefits
  • Reduced network dependency, avoid data latency and data bandwidth requirement.
  • Full access/device control remotely, can avoid operator’s site visit and reduce maintenance cost.
  • Feasible to connect and monitor wireless sensors, smart field sensors, IP Cameras. with benefits out from all such added features, it justifies the overall system solution cost.
  • Predictive maintenance saves part failure recovery cost and reduce system downtime.
  • Better Data privacy/security as all critical field data remains at edge device itself.
  • Single device solution saves overall system cost, cloud components cost, integration efforts & time.
Conclusion

With Edge Computing and Analytics tools implemented with Edge Platform, data processing can be faster, responsive action can be taken in real time, overall system monitoring and data analysis for end user becomes simpler as conclusive notifications they receive in alert form. All achieved with minimal system components that enhances system reliability and optimizes solution cost.