Standard quality-control methodologies such as Six Sigma are being disrupted by the large volume of harvestable sensor and geospatial data, cloud computing platforms and distributed processing systems. Manufacturing industry players are turning to advanced analytics to substitute processes that were prone to failure due to human errors. Smart Factories have better use of resources, less machine and production line downtime and fewer quality issues among other advantages. Yet digital transformation hinders risks posed by disparate systems, the variability in production processes, and uncertainties regarding the whereabouts of the data.
Databricks’ fully managed platform helps companies tackle technology and data silos by leveraging the cloud to scale rapidly, hosting massive amounts of data effortlessly, and by offering streamline workflows for better collaboration between business executives, data scientists, and engineers. A critical area where digital transformation leads to substantial improvements in industrial practices is predictive maintenance (PdM), which prevents failures and optimizes capital equipment upkeep that in turn can yield up to a 10x ROI, increased productivity, and an average of 12% reduction in costs.
However, realizing the advantages of data-driven analytics through the myriad of sensor data does not come without a challenge. How do we build an adept interconnected system that strings it all together and in turn magnifies the likelihood of detecting failures and the condition of in-service equipment?
Steam engine based mechanization revolution
Electricity based mass production revolution
Computer/Internet based knowledge and information
Prior industrial revolutions proved that for maintaining competitiveness, industry players have to adapt to change. Industry 4.0 is not the question of if but the question of when businesses will decide on implementing and later benefiting from the operational advantages of sensors, IoT, cloud computing, advanced algorithms, big data, smart sensors, AI and automation. As opposed to previous industrial revolutions that transformed the way of work – the Forth Revolution transforms the way we think about the past, present, and the future.
The price of sensor, compute power and storage is going down while the available data is getting larger. Letting the machines process all the available information and make decisions instead of humans can provide businesses with increased precision and a competitive edge over other industry players that do not move along the lines of the revolution. The top verticals leading the 4th Industrial Revolution are manufacturing, energy, logistics and aerospace, however other industries benefit from it as well.
THE OPERATIONAL BARRIER
Businesses have to take the several barriers to entry to predicative maintenance into account to make an informed decision before deciding on adopting to advanced analytics or relying on human labor. Industry players face the issue of their assets undergoing degradation that in turn results in shutdowns and periodically unavailable systems.
FIXING ASSET-RELATED ISSUES AS THEY APPEAR RESULTS IN FUTURE FAILURES HAPPENING AT UNCERTAIN POINTS IN TIME THAT DETERIORATE THE EFFECTIVENESS OF INDUSTRIAL PROCESSES.
On the one hand, relying on industry experts’ opinions regarding the estimation of the Residual Useful Life (RUL) of an asset lacks precision. Switching items, components or systems earlier than necessary is not cost-effective as underutilized capacities are wasted. On the other hand, asset or maintenance related failures caused by human errors slow down the entire production process for unpredictably long periods of time. When an asset fails, finding out the whereabout and the root causes of the failure is not always obvious, that delays maintenance practices
THE FINANCIAL BARRIER
THE FREQUENCY OF ASSET FAILURES WHEN HIGH ENOUGH THROUGHOUT THE ASSETS’ LIFETIMES JUSTIFY THE IMPLEMENTATION OF PREDICTIVE MAINTENANCE.
Predictive Maintenance hinders investment related risks posed by the acquisition of the required equipment; such as sensors, control systems, operational systems or network routers that will allow industry players to collect relevant data. The installation of sensors, IoT gateway, cloud based IoT platform, preparation and maintenance of models, consulting, internal and external people outsourcing costs makes the dilemma complicated as to whether adapt or not to adapt to the automation of manufacturing and industrial processes.
THE TECHNOLOGY BARRIER
FIXING ASSET-RELATED ISSUES AS THEY APPEAR RESULTS IN FUTURE FAILURES HAPPENING AT UNCERTAIN POINTS IN TIME THAT DETERIORATE THE EFFECTIVENESS OF INDUSTRIAL PROCESSES.
A variability of extremely sensitive sensor data is being ingested at high velocity, stored for long periods, and classified into different categories. Afterwards, data is being combined with other data sources. This results in an extremely challenging scenario in which teams must manage the data along with machine learning artifacts that have their own lifecycles. Moreover, the end-to-end data platform has to be optimized for streaming speed and latency, data lake scalability and affordability as well as data warehouse reliability and performance.
These challenges are tackled by Databricks’ Managed Delta Lake. At the core, Delta Lake is a bunch of delta tables along with parquet and transactional files. Each transaction gets logged into _’delta_log’ with which ACID transactions are added. Being able to time travel between different versions is of great importance as teams are able to roll back in time and get to see the timestamp and version of each and every ingest that occurred. This provides machine learning teams and practitioners a way to collaborate and test vast amounts of models and experiments without losing track of the different versions. Delta Lake also offers schema enforcement with which the evolution of the schema is controlled, and each change gets checked for compatibility offering a simpler, cleaner and more efficient work environment. On top, teams are equipped with unified batch and streaming source and sink all the way in each table.
DATABRICKS IN YOUR WEAPONARY
DATABRICKS ENABLES BUSINESSES TO BUILD FROM SENSOR AND HISTORICAL DATA DISCOVERY UP TO PRODUCTION LEVEL WITH ITS UNIFIED PLATFORM AND END-TO END MACHINE LEARNING SOLUTIONS.
By leaving on-premises infrastructures behind and taking advantage of the power of cloud computing platform technologies such as Microsoft Azure, businesses gain substantially larger compute power at lower costs.
Databricks minimizes time-related investments and eliminates the complexities of setting up the underlying PdM infrastructure by taking advantage of Azure’s compute and storage resources among other capabilities. Sensor data are getting ingested in various formats and qualities – unstructured, semi-structured or structured – that isolate analytical processes.
Taking the arrival time of the data factor into account, teams are faced with historical, batch and real-time variations. These challenges have to be dealt with in a collaborative nature using a variety of tools and languages. Teams are divided up to focus on different parts of these challenges, such as managing the data, reporting, or building out machine learning models.
Databricks’ unified platform provides a way for data engineers, machine learning practitioners, business executives and analysts to work together in interactive notebook environments. Having access to a fully managed Spark environment gives teams real-time and batch data processing, as well as machine learning capabilities in just a few clicks. Several runtime versions are included with preconfigured clusters that come with all the libraries and components that are necessary throughout the Predictive Maintenance journey.
Databricks is deeply integrated with all the essential Microsoft Azure services such as Azure Data Factory, Azure Data Lake Storage, Azure Machine Learning and Power BI while providing enterprise-grade security.
A variability of extremely sensitive sensor data is being ingested at high velocity, stored for long periods, and classified into different categories. Afterwards, data is being combined with other data sources. This results in an extremely challenging scenario in which teams must manage the data along with machine learning artifacts that have their own lifecycles. Moreover, the end-to-end data platform has to be optimized for streaming speed and latency, data lake scalability and affordability as well as data warehouse reliability and performance.
These challenges are tackled by Databricks’ Managed Delta Lake. At the core, Delta Lake is a bunch of delta tables along with parquet and transactional files. Each transaction gets logged into _’delta_log’ with which ACID transactions are added. Being able to time travel between different versions is of great importance as teams are able to roll back in time and get to see the timestamp and version of each and every ingest that occurred. This provides machine learning teams and practitioners a way to collaborate and test vast amounts of models and experiments without losing track of the different versions.
Delta Lake also offers schema enforcement with which the evolution of the schema is controlled, and each change gets checked for compatibility offering a simpler, cleaner and more efficient work environment. On top, teams are equipped with unified batch and streaming source and sink all the way in each table.
Databricks has produced an enormous amount of value for Shell. It serves as the platform supporting the global deployment of our inventory optimization tool, which is delivering millions of dollars of savings every year.
Daniel Jeavons, General Manager
Advanced Analytics CoE, Shell
The benefits of predictive maintenance are prodigious. On the operational side, it improves the overall equipment efficiency (OEE), enables root cause analysis of systems with which issues are detectable prior to the actual failures happening. Maintenance practices related costs decrease by up to 50%, mean time to repair (MTTR) gets decreased by 60% while pre-determined failures enable businesses to get all the parts that are not in stock but will be used during maintenance – in turn cutting downtime and opening doors for purchasing spare parts at better prices. Industry players can determine the mean time between failures (MTBF) and figure out the most cost-efficient times to replace parts. As the process systems’ availability improve with predictive maintenance, businesses gain as high as a 30% increase in production efficiency. PdM in some scenarios is used to predict system or machine failure that would lead to injury or death; thus, companies’ collective operational safety improves.
Read More on how customers use Databricks in their PdM journey
Sensors are difficult to be placed, limited to a specific area that might not take up the data from the entire machine and are extremely sensitive. There are currently various ongoing researches about radio frequency signals that can examine machines from afar. Whether or not this will be a standard practice in the future, predictive maintenance via sensor technology already provides unquestionable benefits to businesses.
We deliver large-scale data processing, analytical and production machine learning solutions globally to achieve substantial improvements in clients' business performance through data.
We have special competence in innovative technologies like Databricks, Microsoft Azure, Amazon Web Services (AWS), and industry-specific use-cases in Manufacturing, Pharmaceuticals, Agriculture, Livestock, and Digital Products.
Contact Us