The document discusses a predictive maintenance solution for heavy machinery that uses machine learning and artificial intelligence. It ingests operational data from cranes, builds ARIMA models to forecast faults across components, and generates proactive alerts to stakeholders to enable timely maintenance actions. This reduces downtime delays and improves utilization by 7%. The solution analyzes historical fault data, predicts imminent issues, and provides alerts by component and machinery to facilitate effective maintenance scheduling and reduce downtime.

1. DIGIOTAI: ML/AI USE CASE
Conditional Monitoring System

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PREDICTIVE MAINTENANCE OF HEAVY MACHINERY
Analyse 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 stake
holders for quick and timely actions
PROBLEM
STATEMENT
Ingest Data Build the Model Generate Insights
Cranes operational data gathered from
various sensors across geo locations was
ingested into AWS storage
Custom ARIMA models
Built to forecast diverse faults across
several bins
Predict the imminent faults of
different components
APPROACH BENEFITS
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

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PREDICTIVE MAINTENANCE CASE STUDY - INDUSTRY 4.0 ML AND AI
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

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CASE STUDY - INDUSTRY 4.0 ML AND AI – PHASES
DATAANALYSIS PHASE
Initial Data Gathering : Heavy Machinery enterprise had over 10,000 Crane
assets enabled with sensors which had to be analysed and as an outcome of
the analysis , a prediction was to be made on probable asset failures and
malfunctioning
Data Categorization : Out of 10,000+ assets a total of 100 million records
were captured over a period of 9 months duration . Out of these 100 million
records about 10 million error records were obtained with over 20 fault codes
and error metrics. This faulty data set was analysed across its length and
breadth to forecast predictions along with survival & sustenance metrics.
FORECAST PHASE
As an outcome of the Data Processing phase , in alignment with the analysis
development phase of the algorithms were accomplished across the buckets of
Classification, Clustering , Forecast and Survival
Time series models – ARIMA and Holt winters was been implemented on
given data. Based on the Data Processing phase findings, fault forecast
analysis has been confined only to emergency stops, Hoist overload and Over
temperature data. Illustrative application of models has been carried-out on
select set of assets
Tool: Used R for time series model building on desktop as well as AWS large
instance. Excel is used to view input and output .csv files
DATA PROCESSING PHASE
AUTOMATION AND SURVIVAL
ANALYSIS PHASE
Overall 9 month of data was zipped with varied datatypes (~10GB), and the
other fault file including the parameter description and other faults was zipped
with csv file (130MB) . This data has been analysed in the data processing
phase along with parameter file that had about 20 parameter descriptions and
almost equal number of fault codes. Data consistency and multi-dimensional
analysis was carried out in this phase across asset-id, fault-code and
time/months.
Tool: Used R for basic file processing and got the basic statistics and
summaries
ARIMA based fault forecast for all assets has been completed with internal fine
tuning and automatic display of performing (Amber and Green) and non-
performing (Red) assets. RAG status has been determined based on the initial
6 months’ average that was used for training the model. Asset
performance/faults predictions have been made for subsequent weeks/months
and highlighted some of the assets under each of R, A, G categories
Tool: Used R/Python for time series model building on desktop as well as AWS
large instance.

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CASE STUDY – TECH COMPONENTS & METRIC PATTERNS
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

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Contact Us
vijay.g@digiotai.com
www.digiotai.com
VIJAY GUNTI
Founder and CTO
swap.m@digiotai.com
www.digiotai.com
SWAP MUKHERJEE
Founder and CEO
CONTACT US