The dashboard was green. The algorithm was confident. Three weeks later, the compressor seized.
Modern maintenance has entered a new era. Sensors watch our equipment around the clock. Algorithms predict failures before they happen. Dashboards display health scores with confident precision. The technology is impressive—and increasingly, it's making decisions that humans used to make.
But what happens when we stop questioning what the machines tell us?
Ghost in the Machine pulls back the curtain on the invisible forces shaping industrial reliability: the data, algorithms, and feedback loops that quietly determine when equipment fails, when it survives, and who is accountable when things go wrong. This is not another book celebrating the promise of AI and predictive analytics. It's a clear-eyed examination of where intelligent systems genuinely help—and where blind trust in technology creates dangerous new failure modes.
Written for maintenance professionals, reliability engineers, and operations leaders, this book explores:
Ghost in the Machine challenges leaders and technicians alike to rethink the relationship between human judgment and automated systems. The machines are learning. The question is whether we'll remain smart enough to stay in charge.
The ghost in modern maintenance isn't the algorithm. It's us—our judgment, our experience, our accountability. This book is about keeping that ghost alive.
"synopsis" may belong to another edition of this title.
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condition: new. Paperback. The dashboard was green. The algorithm was confident. Three weeks later, the compressor seized.Modern maintenance has entered a new era. Sensors watch our equipment around the clock. Algorithms predict failures before they happen. Dashboards display health scores with confident precision. The technology is impressive-and increasingly, it's making decisions that humans used to make.But what happens when we stop questioning what the machines tell us?Ghost in the Machine pulls back the curtain on the invisible forces shaping industrial reliability: the data, algorithms, and feedback loops that quietly determine when equipment fails, when it survives, and who is accountable when things go wrong. This is not another book celebrating the promise of AI and predictive analytics. It's a clear-eyed examination of where intelligent systems genuinely help-and where blind trust in technology creates dangerous new failure modes.Written for maintenance professionals, reliability engineers, and operations leaders, this book explores: Why data is never neutral-and how bias gets baked into sensors, thresholds, and alertsHow feedback loops silently train people to ignore warnings and normalize bad signalsWhat predictive maintenance actually does well, and what vendors won't tell youWhy experienced technicians get overruled by charts-and what organizations lose when they doHow "perfect" systems fail harder than imperfect onesWho is accountable when an algorithm misses a failureGhost in the Machine challenges leaders and technicians alike to rethink the relationship between human judgment and automated systems. The machines are learning. The question is whether we'll remain smart enough to stay in charge.The ghost in modern maintenance isn't the algorithm. It's us-our judgment, our experience, our accountability. This book is about keeping that ghost alive. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798261958703
Seller: California Books, Miami, FL, U.S.A.
Condition: New. Print on Demand. Seller Inventory # I-9798261958703
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # L2-9798261958703
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # L2-9798261958703
Quantity: Over 20 available
Seller: CitiRetail, Stevenage, United Kingdom
Paperback. Condition: new. Paperback. The dashboard was green. The algorithm was confident. Three weeks later, the compressor seized.Modern maintenance has entered a new era. Sensors watch our equipment around the clock. Algorithms predict failures before they happen. Dashboards display health scores with confident precision. The technology is impressive-and increasingly, it's making decisions that humans used to make.But what happens when we stop questioning what the machines tell us?Ghost in the Machine pulls back the curtain on the invisible forces shaping industrial reliability: the data, algorithms, and feedback loops that quietly determine when equipment fails, when it survives, and who is accountable when things go wrong. This is not another book celebrating the promise of AI and predictive analytics. It's a clear-eyed examination of where intelligent systems genuinely help-and where blind trust in technology creates dangerous new failure modes.Written for maintenance professionals, reliability engineers, and operations leaders, this book explores: Why data is never neutral-and how bias gets baked into sensors, thresholds, and alertsHow feedback loops silently train people to ignore warnings and normalize bad signalsWhat predictive maintenance actually does well, and what vendors won't tell youWhy experienced technicians get overruled by charts-and what organizations lose when they doHow "perfect" systems fail harder than imperfect onesWho is accountable when an algorithm misses a failureGhost in the Machine challenges leaders and technicians alike to rethink the relationship between human judgment and automated systems. The machines are learning. The question is whether we'll remain smart enough to stay in charge.The ghost in modern maintenance isn't the algorithm. It's us-our judgment, our experience, our accountability. This book is about keeping that ghost alive. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798261958703
Quantity: 1 available