ABSTRACT

RESTORING INHERENT SYSTEM RELIABILITY: HARNESSING APM AND ADVANCED ANALYTICS

In today’s digital era, asset-intensive industries such as utilities are continuously undergoing profound changes for several reasons such as digital transformation adoption, increased safety standards, knowledge capture of an aging workforce, life extension of aging assets, ISO 55000 series standard adoption (with its plan, do, check, act methodology), ESG goals etc. To better meet the needs of the organizations, Asset Management is experiencing an intelligence evolution through innovative solutions led by Industrial Internet of Things (IIoT) technologies. Enhanced machine learning (ML) and expanded reliability modelling capabilities in Asset Performance Management (APM) applications, secure and intelligent edge devices are considered as "must-have” features. This technology is now key, as Asset Intensive organizations can ensure not only operational resilience and business profitability but also reliability & safety of both equipment and mobile workforce.

Predicting future condition of assets and restoring the inherent system reliability is easier when data are immediately available and automatically assessed. However, utilities often face the challenge that the right data cannot be gathered from the field easily. Assets may be spread across the country, hence may be difficult or expensive to reach, while are exposed to weather changes and harsh environmental conditions. Furthermore, inspections of assets in the field are usually based on qualitative data (wear and integrity of the component), and execution can be hazardous, expensive, and time consuming, while only providing a snapshot in time.

The challenge of reaching the highest peak of asset management excellence can be tackled by an innovative APM solution that combines:

- State of art reliability models: - incorporates Subject Matter Experts (SME) expertise in the field of Failure Modes and Effects Analysis (FMEA) of such assets with most innovative technologies based on Artificial Intelligence (AI)

- Ability to perform asset life cycle assessments: - incorporates predictions of such reliability models to simulate different kinds of future interventions, continuously improving performance of assets while balancing risk and associated costs.

In my presentation, I will discuss how Hitachi Energy’s Lumada APM assists our customers in maximizing value generation across their entire energy value chain. Additionally, I will outline a phase-wise strategy to help asset- intensive organizations transition toward an advanced asset management practice based on system reliability and financial optimization.

BIO

Siddhanta Suryabanshi currently works in a Senior Pre-sales consultant role in EMEA, part of asset management advisory in the region. He is focused on Asset Performance Management (APM) offering of Hitachi Energy’s Asset Management portfolio. Siddhanta has 7+ years of experience in:

- Oil and Gas industry - focused mainly on operations and process efficiency.

- Aerospace industry - focused mainly on Supply chain and procurement.

- Utility industry - focused mainly on Asset Management, Asset life cycle management etc.
 

Siddhanta holds a full-time MBA degree in general management and a bachelor’s degree in chemical engineering. He is also a certified Green belt in Lean Six Sigma.